{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyCaret 2 Anomaly Example\n",
    "This notebook is created using PyCaret 2.0. Last updated : 28-07-2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pycaret-nightly-0.39\n"
     ]
    }
   ],
   "source": [
    "# check version\n",
    "from pycaret.utils import version\n",
    "version()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "      <th>Col6</th>\n",
       "      <th>Col7</th>\n",
       "      <th>Col8</th>\n",
       "      <th>Col9</th>\n",
       "      <th>Col10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.263995</td>\n",
       "      <td>0.764929</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.935242</td>\n",
       "      <td>0.605867</td>\n",
       "      <td>0.518790</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.723782</td>\n",
       "      <td>0.733591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546092</td>\n",
       "      <td>0.653975</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.227772</td>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.837066</td>\n",
       "      <td>0.272379</td>\n",
       "      <td>0.331679</td>\n",
       "      <td>0.429297</td>\n",
       "      <td>0.367422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.336714</td>\n",
       "      <td>0.538842</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>0.365792</td>\n",
       "      <td>0.861309</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>0.088600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092108</td>\n",
       "      <td>0.995017</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.514724</td>\n",
       "      <td>0.562208</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.073715</td>\n",
       "      <td>0.208463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325261</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>0.957033</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.307923</td>\n",
       "      <td>0.355315</td>\n",
       "      <td>0.501899</td>\n",
       "      <td>0.558449</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.182754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
       "0  0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
       "1  0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
       "2  0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
       "3  0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
       "4  0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
       "\n",
       "       Col8      Col9     Col10  \n",
       "0  0.608234  0.723782  0.733591  \n",
       "1  0.331679  0.429297  0.367422  \n",
       "2  0.861309  0.899017  0.088600  \n",
       "3  0.158963  0.073715  0.208463  \n",
       "4  0.558449  0.885169  0.182754  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "data = get_data('anomaly')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Initialize Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83da\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow0_col0\" class=\"data row0 col0\" >session_id </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow1_col0\" class=\"data row1 col0\" >Original Data </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow1_col1\" class=\"data row1 col1\" >(1000, 10)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow2_col0\" class=\"data row2 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow2_col1\" class=\"data row2 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow3_col0\" class=\"data row3 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow3_col1\" class=\"data row3 col1\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow4_col0\" class=\"data row4 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow4_col1\" class=\"data row4 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow5_col0\" class=\"data row5 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow5_col1\" class=\"data row5 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow6_col0\" class=\"data row6 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow6_col1\" class=\"data row6 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow7_col0\" class=\"data row7 col0\" >Transformed Data </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow7_col1\" class=\"data row7 col1\" >(1000, 10)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow8_col0\" class=\"data row8 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow8_col1\" class=\"data row8 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow9_col0\" class=\"data row9 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow9_col1\" class=\"data row9 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow10_col0\" class=\"data row10 col0\" >Normalize </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow10_col1\" class=\"data row10 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow11_col0\" class=\"data row11 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow11_col1\" class=\"data row11 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow12_col0\" class=\"data row12 col0\" >Transformation </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow12_col1\" class=\"data row12 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow13_col0\" class=\"data row13 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow13_col1\" class=\"data row13 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow14_col0\" class=\"data row14 col0\" >PCA </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow14_col1\" class=\"data row14 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow15_col0\" class=\"data row15 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow15_col1\" class=\"data row15 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow16_col0\" class=\"data row16 col0\" >PCA components </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow17_col0\" class=\"data row17 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow18_col0\" class=\"data row18 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow18_col1\" class=\"data row18 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow19_col0\" class=\"data row19 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow19_col1\" class=\"data row19 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow20_col0\" class=\"data row20 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow20_col1\" class=\"data row20 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow21_col0\" class=\"data row21 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow21_col1\" class=\"data row21 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow22_col0\" class=\"data row22 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow22_col1\" class=\"data row22 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83dalevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow23_col0\" class=\"data row23 col0\" >Group Features </td>\n",
       "                        <td id=\"T_1c42de5a_d1a2_11ea_947c_482ae32b83darow23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2f82ef1b470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.anomaly import *\n",
    "ano1 = setup(data, session_id=123, log_experiment=True, experiment_name='anomaly1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Reference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>abod</th>\n",
       "      <td>Angle-base Outlier Detection</td>\n",
       "      <td>pyod.models.abod.ABOD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iforest</th>\n",
       "      <td>Isolation Forest</td>\n",
       "      <td>pyod.models.iforest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <td>Clustering-Based Local Outlier</td>\n",
       "      <td>pyod.models.cblof</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cof</th>\n",
       "      <td>Connectivity-Based Outlier Factor</td>\n",
       "      <td>pyod.models.cof</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>histogram</th>\n",
       "      <td>Histogram-based Outlier Detection</td>\n",
       "      <td>pyod.models.hbos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>k-Nearest Neighbors Detector</td>\n",
       "      <td>pyod.models.knn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lof</th>\n",
       "      <td>Local Outlier Factor</td>\n",
       "      <td>pyod.models.lof</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svm</th>\n",
       "      <td>One-class SVM detector</td>\n",
       "      <td>pyod.models.ocsvm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pca</th>\n",
       "      <td>Principal Component Analysis</td>\n",
       "      <td>pyod.models.pca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcd</th>\n",
       "      <td>Minimum Covariance Determinant</td>\n",
       "      <td>pyod.models.mcd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sod</th>\n",
       "      <td>Subspace Outlier Detection</td>\n",
       "      <td>pyod.models.sod</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sos</th>\n",
       "      <td>Stochastic Outlier Selection</td>\n",
       "      <td>pyod.models.sos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Name              Reference\n",
       "ID                                                                 \n",
       "abod            Angle-base Outlier Detection  pyod.models.abod.ABOD\n",
       "iforest                     Isolation Forest    pyod.models.iforest\n",
       "cluster       Clustering-Based Local Outlier      pyod.models.cblof\n",
       "cof        Connectivity-Based Outlier Factor        pyod.models.cof\n",
       "histogram  Histogram-based Outlier Detection       pyod.models.hbos\n",
       "knn             k-Nearest Neighbors Detector        pyod.models.knn\n",
       "lof                     Local Outlier Factor        pyod.models.lof\n",
       "svm                   One-class SVM detector      pyod.models.ocsvm\n",
       "pca             Principal Component Analysis        pyod.models.pca\n",
       "mcd           Minimum Covariance Determinant        pyod.models.mcd\n",
       "sod               Subspace Outlier Detection        pyod.models.sod\n",
       "sos             Stochastic Outlier Selection        pyod.models.sos"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "iforest = create_model('iforest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = create_model('knn', fraction = 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Assign Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "      <th>Col6</th>\n",
       "      <th>Col7</th>\n",
       "      <th>Col8</th>\n",
       "      <th>Col9</th>\n",
       "      <th>Col10</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.263995</td>\n",
       "      <td>0.764929</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.935242</td>\n",
       "      <td>0.605867</td>\n",
       "      <td>0.518790</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.723782</td>\n",
       "      <td>0.733591</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.035865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546092</td>\n",
       "      <td>0.653975</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.227772</td>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.837066</td>\n",
       "      <td>0.272379</td>\n",
       "      <td>0.331679</td>\n",
       "      <td>0.429297</td>\n",
       "      <td>0.367422</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.084927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.336714</td>\n",
       "      <td>0.538842</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>0.365792</td>\n",
       "      <td>0.861309</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>0.088600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092108</td>\n",
       "      <td>0.995017</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.514724</td>\n",
       "      <td>0.562208</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.073715</td>\n",
       "      <td>0.208463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.042415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325261</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>0.957033</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.307923</td>\n",
       "      <td>0.355315</td>\n",
       "      <td>0.501899</td>\n",
       "      <td>0.558449</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.182754</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.023408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "       Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
       "0  0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
       "1  0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
       "2  0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
       "3  0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
       "4  0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
       "\n",
       "       Col8      Col9     Col10  Label     Score  \n",
       "0  0.608234  0.723782  0.733591      0 -0.035865  \n",
       "1  0.331679  0.429297  0.367422      0 -0.084927  \n",
       "2  0.861309  0.899017  0.088600      1  0.025356  \n",
       "3  0.158963  0.073715  0.208463      1  0.042415  \n",
       "4  0.558449  0.885169  0.182754      0 -0.023408  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iforest_results = assign_model(iforest)\n",
    "iforest_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Analyze Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('dbc1aadf-12f3-4c20-b60f-cc681f2c2615');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(iforest, plot = 'umap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Predict Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Col1</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.263995</td>\n",
       "      <td>0.764929</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.935242</td>\n",
       "      <td>0.605867</td>\n",
       "      <td>0.518790</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.723782</td>\n",
       "      <td>0.733591</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.035865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546092</td>\n",
       "      <td>0.653975</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.227772</td>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.837066</td>\n",
       "      <td>0.272379</td>\n",
       "      <td>0.331679</td>\n",
       "      <td>0.429297</td>\n",
       "      <td>0.367422</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.084927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.336714</td>\n",
       "      <td>0.538842</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>0.365792</td>\n",
       "      <td>0.861309</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>0.088600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092108</td>\n",
       "      <td>0.995017</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.514724</td>\n",
       "      <td>0.562208</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.073715</td>\n",
       "      <td>0.208463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.042415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325261</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>0.957033</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.307923</td>\n",
       "      <td>0.355315</td>\n",
       "      <td>0.501899</td>\n",
       "      <td>0.558449</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.182754</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.023408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
       "0  0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
       "1  0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
       "2  0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
       "3  0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
       "4  0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
       "\n",
       "       Col8      Col9     Col10  Label     Score  \n",
       "0  0.608234  0.723782  0.733591      0 -0.035865  \n",
       "1  0.331679  0.429297  0.367422      0 -0.084927  \n",
       "2  0.861309  0.899017  0.088600      1  0.025356  \n",
       "3  0.158963  0.073715  0.208463      1  0.042415  \n",
       "4  0.558449  0.885169  0.182754      0 -0.023408  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_new = predict_model(iforest, data=data)\n",
    "pred_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Save / Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(iforest, model_name='iforest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Sucessfully Loaded\n",
      "[Pipeline(memory=None,\n",
      "         steps=[('dtypes',\n",
      "                 DataTypes_Auto_infer(categorical_features=[],\n",
      "                                      display_types=True, features_todrop=[],\n",
      "                                      ml_usecase='regression',\n",
      "                                      numerical_features=[],\n",
      "                                      target='dummy_target',\n",
      "                                      time_features=[])),\n",
      "                ('imputer',\n",
      "                 Simple_Imputer(categorical_strategy='not_available',\n",
      "                                numeric_strategy='mean',\n",
      "                                target_variable=None)),\n",
      "                ('new_levels1',\n",
      "                 New_Catagorical_L...\n",
      "                                                    target='dummy_target')),\n",
      "                ('feature_time',\n",
      "                 Make_Time_Features(list_of_features=None, time_feature=[])),\n",
      "                ('group', Empty()), ('scaling', Empty()),\n",
      "                ('P_transform', Empty()), ('binn', Empty()),\n",
      "                ('fix_perfect', Empty()), ('rem_outliers', Empty()),\n",
      "                ('dummy', Dummify(target='dummy_target')),\n",
      "                ('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\n",
      "                ('pca', Empty())],\n",
      "         verbose=False), IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
      "    max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\n",
      "    random_state=123, verbose=0)]\n"
     ]
    }
   ],
   "source": [
    "loaded_iforest = load_model('iforest')\n",
    "print(loaded_iforest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"1face1b5-c2d7-4d75-bdb8-5b7054c80c5b\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"1face1b5-c2d7-4d75-bdb8-5b7054c80c5b\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(memory=None,\n",
       "         steps=[('dtypes',\n",
       "                 DataTypes_Auto_infer(categorical_features=[],\n",
       "                                      display_types=True, features_todrop=[],\n",
       "                                      ml_usecase='regression',\n",
       "                                      numerical_features=[],\n",
       "                                      target='dummy_target',\n",
       "                                      time_features=[])),\n",
       "                ('imputer',\n",
       "                 Simple_Imputer(categorical_strategy='not_available',\n",
       "                                numeric_strategy='mean',\n",
       "                                target_variable=None)),\n",
       "                ('new_levels1',\n",
       "                 New_Catagorical_L...\n",
       "                                                    target='dummy_target')),\n",
       "                ('feature_time',\n",
       "                 Make_Time_Features(list_of_features=None, time_feature=[])),\n",
       "                ('group', Empty()), ('scaling', Empty()),\n",
       "                ('P_transform', Empty()), ('binn', Empty()),\n",
       "                ('fix_perfect', Empty()), ('rem_outliers', Empty()),\n",
       "                ('dummy', Dummify(target='dummy_target')),\n",
       "                ('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\n",
       "                ('pca', Empty())],\n",
       "         verbose=False)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"123a7819-c42c-47d1-bdd3-e8770d4717d4\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"123a7819-c42c-47d1-bdd3-e8770d4717d4\">DataTypes_Auto_infer</label><div class=\"sk-toggleable__content\"><pre>DataTypes_Auto_infer(ml_usecase='regression', target='dummy_target')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"1338b1be-bf32-49fe-b149-26877e30e2ef\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"1338b1be-bf32-49fe-b149-26877e30e2ef\">Simple_Imputer</label><div class=\"sk-toggleable__content\"><pre>Simple_Imputer(categorical_strategy='not_available', numeric_strategy='mean',\n",
       "               target_variable=None)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"bb0ecfa3-18f8-4dd2-b980-265d6121fec2\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"bb0ecfa3-18f8-4dd2-b980-265d6121fec2\">New_Catagorical_Levels_in_TestData</label><div class=\"sk-toggleable__content\"><pre>New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='dummy_target')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"506edc67-e60f-4005-9897-1ea975a88a6d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"506edc67-e60f-4005-9897-1ea975a88a6d\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"47be94d7-e884-4423-987f-332e21c70b08\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"47be94d7-e884-4423-987f-332e21c70b08\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"41154a43-34b5-402a-b684-0d404cbe3169\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"41154a43-34b5-402a-b684-0d404cbe3169\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"7b2e1fef-d27e-4f6c-93dd-b7a5f586798d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"7b2e1fef-d27e-4f6c-93dd-b7a5f586798d\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"5277e7f1-1164-4d75-a9c5-ceac970aa8b5\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"5277e7f1-1164-4d75-a9c5-ceac970aa8b5\">New_Catagorical_Levels_in_TestData</label><div class=\"sk-toggleable__content\"><pre>New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='dummy_target')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3421a4d1-c8a9-40ec-95b1-6836d904f99e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"3421a4d1-c8a9-40ec-95b1-6836d904f99e\">Make_Time_Features</label><div class=\"sk-toggleable__content\"><pre>Make_Time_Features(list_of_features=None)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"ada7883b-fb49-4cb6-97bb-0c7556c762bf\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"ada7883b-fb49-4cb6-97bb-0c7556c762bf\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"757159cf-609f-412f-8756-381c77717705\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"757159cf-609f-412f-8756-381c77717705\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c2ee072c-784a-4aa6-bdcc-6659f15749d7\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"c2ee072c-784a-4aa6-bdcc-6659f15749d7\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"abe13d6c-2bcd-4ea5-a83e-a93920b5621e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"abe13d6c-2bcd-4ea5-a83e-a93920b5621e\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8bc078e2-9d45-4791-9e3e-305dd76e5548\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"8bc078e2-9d45-4791-9e3e-305dd76e5548\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"fe546452-c5b5-41cf-92ff-0f7032b133d8\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"fe546452-c5b5-41cf-92ff-0f7032b133d8\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"9d7f33c1-a849-4c02-a496-883630e95174\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"9d7f33c1-a849-4c02-a496-883630e95174\">Dummify</label><div class=\"sk-toggleable__content\"><pre>Dummify(target='dummy_target')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"22c1feb2-fe51-4f6c-8c9e-9a17ae5a3b62\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"22c1feb2-fe51-4f6c-8c9e-9a17ae5a3b62\">Clean_Colum_Names</label><div class=\"sk-toggleable__content\"><pre>Clean_Colum_Names()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"99f96b82-1566-49a4-b64c-85c41ebdd8f5\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"99f96b82-1566-49a4-b64c-85c41ebdd8f5\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"6ce9bc65-3916-43bf-bc7d-9911a1490e63\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"6ce9bc65-3916-43bf-bc7d-9911a1490e63\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('dtypes',\n",
       "                 DataTypes_Auto_infer(categorical_features=[],\n",
       "                                      display_types=True, features_todrop=[],\n",
       "                                      ml_usecase='regression',\n",
       "                                      numerical_features=[],\n",
       "                                      target='dummy_target',\n",
       "                                      time_features=[])),\n",
       "                ('imputer',\n",
       "                 Simple_Imputer(categorical_strategy='not_available',\n",
       "                                numeric_strategy='mean',\n",
       "                                target_variable=None)),\n",
       "                ('new_levels1',\n",
       "                 New_Catagorical_L...\n",
       "                                                    target='dummy_target')),\n",
       "                ('feature_time',\n",
       "                 Make_Time_Features(list_of_features=None, time_feature=[])),\n",
       "                ('group', Empty()), ('scaling', Empty()),\n",
       "                ('P_transform', Empty()), ('binn', Empty()),\n",
       "                ('fix_perfect', Empty()), ('rem_outliers', Empty()),\n",
       "                ('dummy', Dummify(target='dummy_target')),\n",
       "                ('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\n",
       "                ('pca', Empty())],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display='diagram')\n",
    "loaded_iforest[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display='text')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Deploy Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Succesfully Deployed on AWS S3\n"
     ]
    }
   ],
   "source": [
    "deploy_model(iforest, model_name = 'iforest-aws', authentication = {'bucket' : 'pycaret-test'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Get Config / Set Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.263995</td>\n",
       "      <td>0.764929</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.935242</td>\n",
       "      <td>0.605867</td>\n",
       "      <td>0.518790</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.723782</td>\n",
       "      <td>0.733591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546092</td>\n",
       "      <td>0.653975</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.227772</td>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.837066</td>\n",
       "      <td>0.272379</td>\n",
       "      <td>0.331679</td>\n",
       "      <td>0.429297</td>\n",
       "      <td>0.367422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.336714</td>\n",
       "      <td>0.538842</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>0.365792</td>\n",
       "      <td>0.861309</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>0.088600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092108</td>\n",
       "      <td>0.995017</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.514724</td>\n",
       "      <td>0.562208</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.073715</td>\n",
       "      <td>0.208463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325261</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>0.957033</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.307923</td>\n",
       "      <td>0.355315</td>\n",
       "      <td>0.501899</td>\n",
       "      <td>0.558449</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.182754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
       "0  0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
       "1  0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
       "2  0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
       "3  0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
       "4  0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
       "\n",
       "       Col8      Col9     Col10  \n",
       "0  0.608234  0.723782  0.733591  \n",
       "1  0.331679  0.429297  0.367422  \n",
       "2  0.861309  0.899017  0.088600  \n",
       "3  0.158963  0.073715  0.208463  \n",
       "4  0.558449  0.885169  0.182754  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = get_config('X')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_config('seed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.anomaly import set_config\n",
    "set_config('seed', 999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "999"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_config('seed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Get System Logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2020-07-29 09', '47', '14,652', 'INFO', 'PyCaret Regression Module']\n",
      "['2020-07-29 09', '47', '14,652', 'INFO', 'version pycaret-nightly-0.39']\n",
      "['2020-07-29 09', '47', '14,652', 'INFO', 'Initializing setup()']\n",
      "['2020-07-29 09', '47', '14,652', 'INFO', 'USI', 'e640']\n",
      "['2020-07-29 09', '47', '14,652', 'INFO', 'setup(data=(1338, 7), target=charges, train_size=0.7, sampling=True, sample_estimator=None, categorical_features=None, categorical_imputation=constant, ordinal_features=None,']\n",
      "['high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, numeric_imputation=mean, date_features=None, ignore_features=None, normalize=False,']\n",
      "['normalize_method=zscore, transformation=False, transformation_method=yeo-johnson, handle_unknown_categorical=True, unknown_categorical_method=least_frequent, pca=False, pca_method=linear,']\n",
      "['pca_components=None, ignore_low_variance=False, combine_rare_levels=False, rare_level_threshold=0.1, bin_numeric_features=None, remove_outliers=False, outliers_threshold=0.05,']\n",
      "['remove_multicollinearity=False, multicollinearity_threshold=0.9, remove_perfect_collinearity=False, create_clusters=False, cluster_iter=20,']\n",
      "['polynomial_features=False, polynomial_degree=2, trigonometry_features=False, polynomial_threshold=0.1, group_features=None,']\n",
      "['group_names=None, feature_selection=False, feature_selection_threshold=0.8, feature_interaction=False, feature_ratio=False, interaction_threshold=0.01, transform_target=False,']\n",
      "['transform_target_method=box-cox, data_split_shuffle=True, folds_shuffle=False, n_jobs=-1, html=True, session_id=123, log_experiment=True,']\n",
      "['experiment_name=insurance1, log_plots=False, log_profile=False, log_data=False, silent=False, verbose=True, profile=False)']\n",
      "['2020-07-29 09', '47', '14,653', 'INFO', 'Checking environment']\n",
      "['2020-07-29 09', '47', '14,653', 'INFO', 'python_version', '3.6.10']\n",
      "['2020-07-29 09', '47', '14,653', 'INFO', 'python_build', \"('default', 'May  7 2020 19\", '46', \"08')\"]\n",
      "['2020-07-29 09', '47', '14,653', 'INFO', 'machine', 'AMD64']\n",
      "['2020-07-29 09', '47', '14,653', 'INFO', 'platform', 'Windows-10-10.0.18362-SP0']\n",
      "['2020-07-29 09', '47', '14,674', 'INFO', 'Memory', 'svmem(total=17032478720, available=5530103808, percent=67.5, used=11502374912, free=5530103808)']\n",
      "['2020-07-29 09', '47', '14,674', 'INFO', 'Physical Core', '4']\n",
      "['2020-07-29 09', '47', '14,674', 'INFO', 'Logical Core', '8']\n",
      "['2020-07-29 09', '47', '14,674', 'INFO', 'Checking libraries']\n",
      "['2020-07-29 09', '47', '14,674', 'INFO', 'pd==1.0.4']\n",
      "['2020-07-29 09', '47', '14,674', 'INFO', 'numpy==1.18.5']\n",
      "['2020-07-29 09', '47', '15,120', 'INFO', 'sklearn==0.23.1']\n",
      "['2020-07-29 09', '47', '15,204', 'INFO', 'xgboost==1.1.1']\n",
      "['2020-07-29 09', '47', '15,259', 'INFO', 'lightgbm==2.3.1']\n",
      "['2020-07-29 09', '47', '15,310', 'INFO', 'catboost==0.23.2']\n",
      "['2020-07-29 09', '47', '15,876', 'INFO', 'mlflow==1.8.0']\n",
      "['2020-07-29 09', '47', '15,877', 'INFO', 'Checking Exceptions']\n",
      "['2020-07-29 09', '47', '15,877', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '47', '15,877', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '47', '15,900', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '47', '18,284', 'INFO', 'Copying data for preprocessing']\n",
      "['2020-07-29 09', '47', '18,285', 'INFO', 'Declaring global variables']\n",
      "['2020-07-29 09', '47', '18,296', 'INFO', 'Declaring preprocessing parameters']\n",
      "['2020-07-29 09', '47', '18,296', 'INFO', 'Importing preprocessing module']\n",
      "['2020-07-29 09', '47', '19,149', 'INFO', 'Creating preprocessing pipeline']\n",
      "['2020-07-29 09', '47', '20,310', 'INFO', 'Preprocessing pipeline created successfully']\n",
      "['2020-07-29 09', '47', '20,310', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '20,310', 'INFO', 'Creating grid variables']\n",
      "['2020-07-29 09', '47', '20,311', 'INFO', 'Creating global containers']\n",
      "['2020-07-29 09', '47', '20,410', 'INFO', 'Logging experiment in MLFlow']\n",
      "['2020-07-29 09', '47', '20,692', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '20,693', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '20,702', 'INFO', 'save_model(model=Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), model_name=Transformation Pipeline, verbose=False)']\n",
      "['2020-07-29 09', '47', '20,702', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '20,710', 'INFO', 'Transformation Pipeline.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '20,721', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), None]']\n",
      "['2020-07-29 09', '47', '20,721', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '20,722', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '20,809', 'INFO', 'create_model_container', '0']\n",
      "['2020-07-29 09', '47', '20,809', 'INFO', 'master_model_container', '0']\n",
      "['2020-07-29 09', '47', '20,809', 'INFO', 'display_container', '0']\n",
      "['2020-07-29 09', '47', '20,809', 'INFO', 'setup() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '32,141', 'INFO', 'Initializing compare_models()']\n",
      "['2020-07-29 09', '47', '32,141', 'INFO', 'compare_models(blacklist=None, whitelist=None, fold=5, round=4, sort=R2, n_select=1, turbo=True, verbose=True)']\n",
      "['2020-07-29 09', '47', '32,141', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '47', '32,141', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '47', '32,141', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '47', '32,174', 'INFO', 'Copying training dataset']\n",
      "['2020-07-29 09', '47', '32,176', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '47', '32,186', 'INFO', 'Importing untrained models']\n",
      "['2020-07-29 09', '47', '32,187', 'INFO', 'Import successful']\n",
      "['2020-07-29 09', '47', '32,191', 'INFO', 'Defining folds']\n",
      "['2020-07-29 09', '47', '32,192', 'INFO', 'Declaring metric variables']\n",
      "['2020-07-29 09', '47', '32,192', 'INFO', 'Initializing Linear Regression']\n",
      "['2020-07-29 09', '47', '32,198', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '32,206', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '32,210', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '32,212', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '32,212', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '32,229', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '32,235', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '32,239', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '32,240', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '32,241', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '32,248', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '32,254', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '32,257', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '32,259', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '32,260', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '32,266', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '32,271', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '32,275', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '32,276', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '32,277', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '32,282', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '32,288', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '32,291', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '32,293', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '32,293', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '32,302', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '32,302', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '32,313', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '32,365', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '32,366', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '32,366', 'INFO', 'save_model(model=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '47', '32,366', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '32,371', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '32,376', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False), None]']\n",
      "['2020-07-29 09', '47', '32,376', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '32,376', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '32,731', 'INFO', 'Initializing Lasso Regression']\n",
      "['2020-07-29 09', '47', '32,736', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '32,742', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '32,747', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '32,749', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '32,749', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '32,756', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '32,762', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '32,766', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '32,768', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '32,768', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '32,775', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '32,781', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '32,786', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '32,788', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '32,788', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '32,795', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '32,801', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '32,804', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '32,805', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '32,805', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '32,812', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '32,818', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '32,820', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '32,822', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '32,822', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '32,829', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '32,829', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '32,841', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '32,901', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '32,901', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '32,901', 'INFO', 'save_model(model=Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,']\n",
      "['normalize=False, positive=False, precompute=False, random_state=123,']\n",
      "[\"selection='cyclic', tol=0.0001, warm_start=False), model_name=Trained Model, verbose=False)\"]\n",
      "['2020-07-29 09', '47', '32,901', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '32,907', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '32,912', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,']\n",
      "['normalize=False, positive=False, precompute=False, random_state=123,']\n",
      "[\"selection='cyclic', tol=0.0001, warm_start=False), None]\"]\n",
      "['2020-07-29 09', '47', '32,912', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '32,912', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '32,961', 'INFO', 'Initializing Ridge Regression']\n",
      "['2020-07-29 09', '47', '32,967', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '32,972', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '32,974', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '32,976', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '32,976', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '32,983', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '32,988', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '32,991', 'INFO', 'Evaluating Metrics']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2020-07-29 09', '47', '32,993', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '32,993', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,001', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '33,006', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,008', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,010', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,010', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,017', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '33,022', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,025', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,027', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,027', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,035', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '33,041', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,043', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,045', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,045', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,051', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '33,051', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '33,062', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '33,129', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '33,129', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '33,129', 'INFO', 'save_model(model=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,']\n",
      "[\"normalize=False, random_state=123, solver='auto', tol=0.001), model_name=Trained Model, verbose=False)\"]\n",
      "['2020-07-29 09', '47', '33,129', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '33,134', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '33,139', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,']\n",
      "[\"normalize=False, random_state=123, solver='auto', tol=0.001), None]\"]\n",
      "['2020-07-29 09', '47', '33,139', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '33,139', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '33,188', 'INFO', 'Initializing Elastic Net']\n",
      "['2020-07-29 09', '47', '33,194', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '33,200', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,203', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,205', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,205', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,212', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '33,219', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,222', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,225', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,225', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,232', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '33,237', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,241', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,243', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,243', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,251', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '33,255', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,260', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,262', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,262', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,270', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '33,277', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,280', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,283', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,283', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,291', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '33,291', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '33,302', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '33,367', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '33,368', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '33,368', 'INFO', 'save_model(model=ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,']\n",
      "['max_iter=1000, normalize=False, positive=False, precompute=False,']\n",
      "[\"random_state=123, selection='cyclic', tol=0.0001, warm_start=False), model_name=Trained Model, verbose=False)\"]\n",
      "['2020-07-29 09', '47', '33,368', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '33,373', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '33,379', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,']\n",
      "['max_iter=1000, normalize=False, positive=False, precompute=False,']\n",
      "[\"random_state=123, selection='cyclic', tol=0.0001, warm_start=False), None]\"]\n",
      "['2020-07-29 09', '47', '33,379', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '33,379', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '33,465', 'INFO', 'Initializing Least Angle Regression']\n",
      "['2020-07-29 09', '47', '33,470', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '33,476', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,482', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,483', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,484', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,490', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '33,496', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,502', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,504', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,504', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,510', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '33,517', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,522', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,523', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,523', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,531', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '33,536', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,542', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,543', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,544', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,552', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '33,564', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,572', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,574', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,574', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,580', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '33,581', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '33,593', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '33,653', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '33,654', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '33,654', 'INFO', 'save_model(model=Lars(copy_X=True, eps=2.220446049250313e-16, fit_intercept=True, fit_path=True,']\n",
      "[\"jitter=None, n_nonzero_coefs=500, normalize=True, precompute='auto',\"]\n",
      "['random_state=None, verbose=False), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '47', '33,654', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '33,658', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '33,664', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), Lars(copy_X=True, eps=2.220446049250313e-16, fit_intercept=True, fit_path=True,']\n",
      "[\"jitter=None, n_nonzero_coefs=500, normalize=True, precompute='auto',\"]\n",
      "['random_state=None, verbose=False), None]']\n",
      "['2020-07-29 09', '47', '33,664', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '33,664', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '33,714', 'INFO', 'Initializing Lasso Least Angle Regression']\n",
      "['2020-07-29 09', '47', '33,720', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '33,728', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,733', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,734', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,734', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,739', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '33,745', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,750', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,752', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,752', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,758', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '33,764', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,768', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,770', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,770', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,775', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '33,780', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,784', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,786', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,786', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,792', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '33,798', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,802', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,803', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,803', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,809', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '33,810', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '33,821', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '33,878', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '33,878', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '33,878', 'INFO', 'save_model(model=LassoLars(alpha=1.0, copy_X=True, eps=2.220446049250313e-16, fit_intercept=True,']\n",
      "['fit_path=True, jitter=None, max_iter=500, normalize=True,']\n",
      "[\"positive=False, precompute='auto', random_state=None, verbose=False), model_name=Trained Model, verbose=False)\"]\n",
      "['2020-07-29 09', '47', '33,878', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '33,884', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '33,889', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), LassoLars(alpha=1.0, copy_X=True, eps=2.220446049250313e-16, fit_intercept=True,']\n",
      "['fit_path=True, jitter=None, max_iter=500, normalize=True,']\n",
      "[\"positive=False, precompute='auto', random_state=None, verbose=False), None]\"]\n",
      "['2020-07-29 09', '47', '33,889', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '33,890', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '33,946', 'INFO', 'Initializing Orthogonal Matching Pursuit']\n",
      "['2020-07-29 09', '47', '33,952', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '33,961', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,964', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,966', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,966', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,974', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '33,983', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '33,985', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '33,987', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '33,988', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '33,997', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '34,002', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '34,006', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '34,008', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '34,008', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '34,017', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '34,023', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '34,026', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '34,029', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '34,029', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '34,037', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '34,044', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '34,048', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '34,051', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '34,051', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '34,058', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '34,058', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '34,073', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '34,193', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '34,193', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '34,194', 'INFO', 'save_model(model=OrthogonalMatchingPursuit(fit_intercept=True, n_nonzero_coefs=None,']\n",
      "[\"normalize=True, precompute='auto', tol=None), model_name=Trained Model, verbose=False)\"]\n",
      "['2020-07-29 09', '47', '34,194', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '34,202', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '34,211', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), OrthogonalMatchingPursuit(fit_intercept=True, n_nonzero_coefs=None,']\n",
      "[\"normalize=True, precompute='auto', tol=None), None]\"]\n",
      "['2020-07-29 09', '47', '34,211', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '34,211', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '34,263', 'INFO', 'Initializing Bayesian Ridge']\n",
      "['2020-07-29 09', '47', '34,269', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '34,277', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '34,284', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '34,286', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '34,287', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '34,295', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '34,304', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '34,311', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '34,314', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '34,314', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '34,323', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '34,331', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '34,339', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '34,341', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '34,342', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '34,350', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '34,357', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '34,364', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '34,367', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '34,367', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '34,375', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '34,383', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '34,391', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '34,393', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '34,393', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '34,402', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '34,402', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '34,420', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '34,507', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '34,507', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '34,508', 'INFO', 'save_model(model=BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, alpha_init=None,']\n",
      "['compute_score=False, copy_X=True, fit_intercept=True,']\n",
      "['lambda_1=1e-06, lambda_2=1e-06, lambda_init=None, n_iter=300,']\n",
      "['normalize=False, tol=0.001, verbose=False), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '47', '34,508', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '34,516', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '34,523', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, alpha_init=None,']\n",
      "['compute_score=False, copy_X=True, fit_intercept=True,']\n",
      "['lambda_1=1e-06, lambda_2=1e-06, lambda_init=None, n_iter=300,']\n",
      "['normalize=False, tol=0.001, verbose=False), None]']\n",
      "['2020-07-29 09', '47', '34,524', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '34,524', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '34,625', 'INFO', 'Initializing Passive Aggressive Regressor']\n",
      "['2020-07-29 09', '47', '34,633', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '34,640', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '34,651', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '34,654', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '34,654', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '34,664', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '34,672', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '34,683', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '34,685', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '34,686', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '34,696', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '34,705', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '34,715', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '34,717', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '34,717', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '34,726', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '34,735', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '34,746', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '34,749', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '34,749', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '34,759', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '34,767', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '34,777', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '34,779', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '34,779', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '34,790', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '34,790', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '34,808', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '34,904', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '34,904', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '34,905', 'INFO', 'save_model(model=PassiveAggressiveRegressor(C=1.0, average=False, early_stopping=False,']\n",
      "['epsilon=0.1, fit_intercept=True,']\n",
      "[\"loss='epsilon_insensitive', max_iter=1000,\"]\n",
      "['n_iter_no_change=5, random_state=123, shuffle=True,']\n",
      "['tol=0.001, validation_fraction=0.1, verbose=0,']\n",
      "['warm_start=False), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '47', '34,905', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '34,914', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '34,922', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), PassiveAggressiveRegressor(C=1.0, average=False, early_stopping=False,']\n",
      "['epsilon=0.1, fit_intercept=True,']\n",
      "[\"loss='epsilon_insensitive', max_iter=1000,\"]\n",
      "['n_iter_no_change=5, random_state=123, shuffle=True,']\n",
      "['tol=0.001, validation_fraction=0.1, verbose=0,']\n",
      "['warm_start=False), None]']\n",
      "['2020-07-29 09', '47', '34,922', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '34,922', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '35,002', 'INFO', 'Initializing Random Sample Consensus']\n",
      "['2020-07-29 09', '47', '35,011', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '35,023', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '35,179', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '35,182', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '35,183', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '35,194', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '35,203', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '35,358', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '35,361', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '35,361', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '35,371', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '35,381', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '35,533', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '35,535', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '35,535', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '35,547', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '35,556', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '35,711', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '35,714', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '35,715', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '35,727', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '35,737', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '35,875', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '35,878', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '35,878', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '35,889', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '35,889', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '35,911', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '36,020', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '36,020', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '36,020', 'INFO', 'save_model(model=RANSACRegressor(base_estimator=None, is_data_valid=None, is_model_valid=None,']\n",
      "[\"loss='absolute_loss', max_skips=inf, max_trials=100,\"]\n",
      "['min_samples=0.5, random_state=123, residual_threshold=None,']\n",
      "['stop_n_inliers=inf, stop_probability=0.99, stop_score=inf), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '47', '36,020', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '36,029', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '36,039', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), RANSACRegressor(base_estimator=None, is_data_valid=None, is_model_valid=None,']\n",
      "[\"loss='absolute_loss', max_skips=inf, max_trials=100,\"]\n",
      "['min_samples=0.5, random_state=123, residual_threshold=None,']\n",
      "['stop_n_inliers=inf, stop_probability=0.99, stop_score=inf), None]']\n",
      "['2020-07-29 09', '47', '36,039', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '36,039', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '36,159', 'INFO', 'Initializing TheilSen Regressor']\n",
      "['2020-07-29 09', '47', '36,168', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '36,178', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '42,054', 'INFO', 'PyCaret Clustering Module']\n",
      "['2020-07-29 09', '47', '42,054', 'INFO', 'version pycaret-nightly-0.39']\n",
      "['2020-07-29 09', '47', '42,055', 'INFO', 'Initializing setup()']\n",
      "['2020-07-29 09', '47', '42,055', 'INFO', 'USI', 'e74c']\n",
      "['2020-07-29 09', '47', '42,056', 'INFO', 'setup(data=(224, 21), categorical_features=None, categorical_imputation=constant, ordinal_features=None, high_cardinality_features=None,']\n",
      "[\"numeric_features=None, numeric_imputation=mean, date_features=None, ignore_features=['Country Name'], normalize=False,\"]\n",
      "['normalize_method=zscore, transformation=False, transformation_method=yeo-johnson, handle_unknown_categorical=True, unknown_categorical_method=least_frequent, pca=False, pca_method=linear,']\n",
      "['pca_components=None, ignore_low_variance=False, combine_rare_levels=False, rare_level_threshold=0.1, bin_numeric_features=None,']\n",
      "['remove_multicollinearity=False, multicollinearity_threshold=0.9, group_features=None,']\n",
      "['group_names=None, supervised=False, supervised_target=None, n_jobs=-1, html=True, session_id=123, log_experiment=True,']\n",
      "['experiment_name=health1, log_plots=True, log_profile=False, log_data=False, silent=False, verbose=True, profile=False)']\n",
      "['2020-07-29 09', '47', '42,057', 'INFO', 'Checking environment']\n",
      "['2020-07-29 09', '47', '42,058', 'INFO', 'python_version', '3.6.10']\n",
      "['2020-07-29 09', '47', '42,058', 'INFO', 'python_build', \"('default', 'May  7 2020 19\", '46', \"08')\"]\n",
      "['2020-07-29 09', '47', '42,059', 'INFO', 'machine', 'AMD64']\n",
      "['2020-07-29 09', '47', '42,060', 'INFO', 'platform', 'Windows-10-10.0.18362-SP0']\n",
      "['2020-07-29 09', '47', '42,272', 'INFO', 'Memory', 'svmem(total=17032478720, available=5177511936, percent=69.6, used=11854966784, free=5177511936)']\n",
      "['2020-07-29 09', '47', '42,272', 'INFO', 'Physical Core', '4']\n",
      "['2020-07-29 09', '47', '42,273', 'INFO', 'Logical Core', '8']\n",
      "['2020-07-29 09', '47', '42,273', 'INFO', 'Checking libraries']\n",
      "['2020-07-29 09', '47', '42,273', 'INFO', 'pd==1.0.4']\n",
      "['2020-07-29 09', '47', '42,283', 'INFO', 'numpy==1.18.5']\n",
      "['2020-07-29 09', '47', '43,243', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '43,252', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '43,252', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '43,276', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '43,295', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '43,989', 'INFO', 'sklearn==0.23.1']\n",
      "['2020-07-29 09', '47', '43,991', 'INFO', 'kmodes==0.10.2']\n",
      "['2020-07-29 09', '47', '45,076', 'INFO', 'PyCaret Anomaly Detection Module']\n",
      "['2020-07-29 09', '47', '45,076', 'INFO', 'version pycaret-nightly-0.39']\n",
      "['2020-07-29 09', '47', '45,076', 'INFO', 'Initializing setup()']\n",
      "['2020-07-29 09', '47', '45,076', 'INFO', 'USI', '9b51']\n",
      "['2020-07-29 09', '47', '45,077', 'INFO', 'setup(data=(1000, 10), categorical_features=None, categorical_imputation=constant, ordinal_features=None, high_cardinality_features=None,']\n",
      "['numeric_features=None, numeric_imputation=mean, date_features=None, ignore_features=None, normalize=False,']\n",
      "['normalize_method=zscore, transformation=False, transformation_method=yeo-johnson, handle_unknown_categorical=True, unknown_categorical_method=least_frequent, pca=False, pca_method=linear,']\n",
      "['pca_components=None, ignore_low_variance=False, combine_rare_levels=False, rare_level_threshold=0.1, bin_numeric_features=None,']\n",
      "['remove_multicollinearity=False, multicollinearity_threshold=0.9, group_features=None,']\n",
      "['group_names=None, supervised=False, supervised_target=None, n_jobs=-1, html=True, session_id=123, log_experiment=True,']\n",
      "['experiment_name=anomaly1, log_plots=False, log_profile=False, log_data=False, silent=False, verbose=True, profile=False)']\n",
      "['2020-07-29 09', '47', '45,077', 'INFO', 'Checking environment']\n",
      "['2020-07-29 09', '47', '45,078', 'INFO', 'python_version', '3.6.10']\n",
      "['2020-07-29 09', '47', '45,078', 'INFO', 'python_build', \"('default', 'May  7 2020 19\", '46', \"08')\"]\n",
      "['2020-07-29 09', '47', '45,078', 'INFO', 'machine', 'AMD64']\n",
      "['2020-07-29 09', '47', '45,079', 'INFO', 'platform', 'Windows-10-10.0.18362-SP0']\n",
      "['2020-07-29 09', '47', '45,083', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '45,089', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '45,090', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '45,113', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '45,133', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '45,136', 'INFO', 'Memory', 'svmem(total=17032478720, available=5155127296, percent=69.7, used=11877351424, free=5155127296)']\n",
      "['2020-07-29 09', '47', '45,137', 'INFO', 'Physical Core', '4']\n",
      "['2020-07-29 09', '47', '45,137', 'INFO', 'Logical Core', '8']\n",
      "['2020-07-29 09', '47', '45,137', 'INFO', 'Checking libraries']\n",
      "['2020-07-29 09', '47', '45,137', 'INFO', 'pd==1.0.4']\n",
      "['2020-07-29 09', '47', '45,138', 'INFO', 'numpy==1.18.5']\n",
      "['2020-07-29 09', '47', '45,767', 'INFO', 'mlflow==1.8.0']\n",
      "['2020-07-29 09', '47', '45,768', 'INFO', 'Checking Exceptions']\n",
      "['2020-07-29 09', '47', '45,769', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '47', '45,850', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '47', '45,903', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '47', '45,903', 'INFO', 'Declaring global variables']\n",
      "['2020-07-29 09', '47', '45,904', 'INFO', 'Copying data for preprocessing']\n",
      "['2020-07-29 09', '47', '45,920', 'INFO', 'Declaring preprocessing parameters']\n",
      "['2020-07-29 09', '47', '45,921', 'INFO', 'Importing preprocessing module']\n",
      "['2020-07-29 09', '47', '47,044', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '47,054', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '47,055', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '47,083', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '47,108', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '48,011', 'WARNING', 'pyod not found']\n",
      "['2020-07-29 09', '47', '48,500', 'INFO', 'Creating preprocessing pipeline']\n",
      "['2020-07-29 09', '47', '49,140', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '49,146', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '49,146', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '49,170', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '49,194', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '49,891', 'INFO', 'Preprocessing pipeline created successfully']\n",
      "['2020-07-29 09', '47', '49,892', 'INFO', 'Creating grid variables']\n",
      "['2020-07-29 09', '47', '49,896', 'INFO', 'Creating global containers']\n",
      "['2020-07-29 09', '47', '49,930', 'INFO', 'mlflow==1.8.0']\n",
      "['2020-07-29 09', '47', '49,931', 'INFO', 'Checking Exceptions']\n",
      "['2020-07-29 09', '47', '49,931', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '47', '50,035', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '47', '50,094', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '47', '50,094', 'INFO', 'Declaring global variables']\n",
      "['2020-07-29 09', '47', '50,094', 'INFO', 'Copying data for preprocessing']\n",
      "['2020-07-29 09', '47', '50,112', 'INFO', 'Declaring preprocessing parameters']\n",
      "['2020-07-29 09', '47', '50,112', 'INFO', 'Importing preprocessing module']\n",
      "['2020-07-29 09', '47', '51,064', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '51,070', 'INFO', 'Logging experiment in MLFlow']\n",
      "['2020-07-29 09', '47', '51,071', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '51,071', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '51,100', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '51,101', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '51,167', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '51,238', 'INFO', 'Creating preprocessing pipeline']\n",
      "['2020-07-29 09', '47', '51,321', 'INFO', 'PyCaret NLP Module']\n",
      "['2020-07-29 09', '47', '51,321', 'INFO', 'version pycaret-nightly-0.39']\n",
      "['2020-07-29 09', '47', '51,322', 'INFO', 'Initializing setup()']\n",
      "['2020-07-29 09', '47', '51,322', 'INFO', 'USI', 'ab65']\n",
      "['2020-07-29 09', '47', '51,322', 'INFO', 'setup(data=(6818, 7), target=en, custom_stopwords=None, html=True, session_id=123, log_experiment=True,']\n",
      "['experiment_name=kiva1, log_plots=True, log_data=False, verbose=True)']\n",
      "['2020-07-29 09', '47', '51,323', 'INFO', 'Checking environment']\n",
      "['2020-07-29 09', '47', '51,323', 'INFO', 'python_version', '3.6.10']\n",
      "['2020-07-29 09', '47', '51,323', 'INFO', 'python_build', \"('default', 'May  7 2020 19\", '46', \"08')\"]\n",
      "['2020-07-29 09', '47', '51,323', 'INFO', 'machine', 'AMD64']\n",
      "['2020-07-29 09', '47', '51,324', 'INFO', 'platform', 'Windows-10-10.0.18362-SP0']\n",
      "['2020-07-29 09', '47', '51,402', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '51,402', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '51,404', 'INFO', 'save_model(model=TheilSenRegressor(copy_X=True, fit_intercept=True, max_iter=300,']\n",
      "['max_subpopulation=10000, n_jobs=-1, n_subsamples=None,']\n",
      "['random_state=123, tol=0.001, verbose=False), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '47', '51,404', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '51,411', 'INFO', 'Memory', 'svmem(total=17032478720, available=5093425152, percent=70.1, used=11939053568, free=5093425152)']\n",
      "['2020-07-29 09', '47', '51,412', 'INFO', 'Physical Core', '4']\n",
      "['2020-07-29 09', '47', '51,412', 'INFO', 'Logical Core', '8']\n",
      "['2020-07-29 09', '47', '51,412', 'INFO', 'Checking libraries']\n",
      "['2020-07-29 09', '47', '51,412', 'INFO', 'pd==1.0.4']\n",
      "['2020-07-29 09', '47', '51,413', 'INFO', 'numpy==1.18.5']\n",
      "['2020-07-29 09', '47', '51,431', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '51,463', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), TheilSenRegressor(copy_X=True, fit_intercept=True, max_iter=300,']\n",
      "['max_subpopulation=10000, n_jobs=-1, n_subsamples=None,']\n",
      "['random_state=123, tol=0.001, verbose=False), None]']\n",
      "['2020-07-29 09', '47', '51,463', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '51,463', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '51,703', 'INFO', 'Initializing Huber Regressor']\n",
      "['2020-07-29 09', '47', '51,722', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '51,746', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '51,869', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '51,870', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '51,897', 'INFO', 'save_model(model=Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True,']\n",
      "[\"features_todrop=['Country Name'],\"]\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "['verbose=False), model_name=Transformation Pipeline, verbose=False)']\n",
      "['2020-07-29 09', '47', '51,897', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '51,912', 'INFO', 'Transformation Pipeline.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '51,922', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '51,926', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '51,926', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '51,942', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True,']\n",
      "[\"features_todrop=['Country Name'],\"]\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "['verbose=False), Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True,']\n",
      "[\"features_todrop=['Country Name'],\"]\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "['verbose=False)]']\n",
      "['2020-07-29 09', '47', '51,942', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '51,943', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '51,951', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '51,969', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '52,110', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '52,118', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '52,118', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '52,131', 'INFO', 'Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True,']\n",
      "[\"features_todrop=['Country Name'],\"]\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "['verbose=False)']\n",
      "['2020-07-29 09', '47', '52,132', 'INFO', 'setup() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '52,142', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '52,165', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '52,296', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '52,301', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '52,302', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '52,321', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '52,340', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '52,469', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '52,473', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '52,473', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '52,491', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '52,507', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '52,632', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '52,638', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '52,638', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '52,653', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '52,654', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '52,688', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '52,825', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '52,825', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '52,826', 'INFO', 'save_model(model=HuberRegressor(alpha=0.0001, epsilon=1.35, fit_intercept=True, max_iter=100,']\n",
      "['tol=1e-05, warm_start=False), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '47', '52,826', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '52,841', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '52,854', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), HuberRegressor(alpha=0.0001, epsilon=1.35, fit_intercept=True, max_iter=100,']\n",
      "['tol=1e-05, warm_start=False), None]']\n",
      "['2020-07-29 09', '47', '52,854', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '52,854', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '53,009', 'INFO', 'Initializing Support Vector Machine']\n",
      "['2020-07-29 09', '47', '53,023', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '53,040', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '53,106', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '53,120', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '53,120', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '53,135', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '53,149', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '53,211', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '53,223', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '53,223', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '53,237', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '53,250', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '53,304', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '53,313', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '53,314', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '53,326', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '53,341', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '53,367', 'INFO', 'gensim==3.8.3']\n",
      "['2020-07-29 09', '47', '53,398', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '53,410', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '53,410', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '53,424', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '53,436', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '53,489', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '53,500', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '53,500', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '53,513', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '53,513', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '53,544', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '53,668', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '53,668', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '53,669', 'INFO', \"save_model(model=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\"]\n",
      "[\"kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False), model_name=Trained Model, verbose=False)\"]\n",
      "['2020-07-29 09', '47', '53,669', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '53,680', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '53,690', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "[\"verbose=False), SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\"]\n",
      "[\"kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False), None]\"]\n",
      "['2020-07-29 09', '47', '53,690', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '53,690', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '53,805', 'INFO', 'Initializing K Neighbors Regressor']\n",
      "['2020-07-29 09', '47', '53,817', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '53,831', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '53,838', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '53,953', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '53,953', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '53,968', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '53,981', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '53,989', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '54,057', 'INFO', 'spacy==2.2.4']\n",
      "['2020-07-29 09', '47', '54,099', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '54,100', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '54,115', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '54,126', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '54,134', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '54,243', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '54,243', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '54,256', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '54,266', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '54,275', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '54,384', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '54,384', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '54,395', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '54,405', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '54,414', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '54,524', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '54,525', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '54,538', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '54,539', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '54,572', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '54,705', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '54,705', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '54,706', 'INFO', \"save_model(model=KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\"]\n",
      "['metric_params=None, n_jobs=-1, n_neighbors=5, p=2,']\n",
      "[\"weights='uniform'), model_name=Trained Model, verbose=False)\"]\n",
      "['2020-07-29 09', '47', '54,707', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '54,723', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '54,740', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "[\"verbose=False), KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\"]\n",
      "['metric_params=None, n_jobs=-1, n_neighbors=5, p=2,']\n",
      "[\"weights='uniform'), None]\"]\n",
      "['2020-07-29 09', '47', '54,740', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '54,740', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '54,857', 'INFO', 'nltk==3.5']\n",
      "['2020-07-29 09', '47', '54,892', 'INFO', 'Initializing Decision Tree']\n",
      "['2020-07-29 09', '47', '54,902', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '54,921', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '54,924', 'INFO', 'textblob==0.15.3']\n",
      "['2020-07-29 09', '47', '54,932', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '54,935', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '54,935', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '54,949', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '54,965', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '54,978', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '54,983', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '54,984', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '55,005', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '55,021', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '55,032', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '55,036', 'INFO', 'Initializing create_model()']\n",
      "['2020-07-29 09', '47', '55,036', 'INFO', 'create_model(model=kmeans, num_clusters=4, ground_truth=None, verbose=True, system=True)']\n",
      "['2020-07-29 09', '47', '55,036', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '47', '55,037', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '47', '55,037', 'INFO', 'Setting num_cluster param']\n",
      "['2020-07-29 09', '47', '55,037', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '47', '55,038', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '55,038', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '55,057', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '55,077', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '55,078', 'INFO', 'Importing untrained model']\n",
      "['2020-07-29 09', '47', '55,078', 'INFO', 'K-Means Clustering Imported succesfully']\n",
      "['2020-07-29 09', '47', '55,094', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '55,098', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '55,102', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '55,102', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '55,122', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '55,141', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '55,158', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '55,163', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '55,163', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '55,189', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '55,190', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '55,214', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '55,232', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '55,236', 'INFO', 'Creating Metrics dataframe']\n",
      "['2020-07-29 09', '47', '55,245', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '55,417', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '55,417', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '55,419', 'INFO', \"save_model(model=DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\"]\n",
      "['max_features=None, max_leaf_nodes=None,']\n",
      "['min_impurity_decrease=0.0, min_impurity_split=None,']\n",
      "['min_samples_leaf=1, min_samples_split=2,']\n",
      "[\"min_weight_fraction_leaf=0.0, presort='deprecated',\"]\n",
      "[\"random_state=123, splitter='best'), model_name=Trained Model, verbose=False)\"]\n",
      "['2020-07-29 09', '47', '55,419', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '55,437', 'INFO', 'SubProcess plot_model() called ==================================']\n",
      "['2020-07-29 09', '47', '55,437', 'INFO', 'Initializing plot_model()']\n",
      "['2020-07-29 09', '47', '55,438', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), plot=cluster, feature=None, label=False, save=True, system=False)']\n",
      "['2020-07-29 09', '47', '55,439', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '55,439', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '47', '55,439', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '47', '55,465', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "[\"verbose=False), DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\"]\n",
      "['max_features=None, max_leaf_nodes=None,']\n",
      "['min_impurity_decrease=0.0, min_impurity_split=None,']\n",
      "['min_samples_leaf=1, min_samples_split=2,']\n",
      "[\"min_weight_fraction_leaf=0.0, presort='deprecated',\"]\n",
      "[\"random_state=123, splitter='best'), None]\"]\n",
      "['2020-07-29 09', '47', '55,466', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '55,466', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '55,656', 'INFO', 'Initializing Random Forest']\n",
      "['2020-07-29 09', '47', '55,675', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '47', '55,694', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '56,059', 'INFO', 'pyLDAvis==2.1.2']\n",
      "['2020-07-29 09', '47', '56,271', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '56,275', 'INFO', 'wordcloud==1.7.0']\n",
      "['2020-07-29 09', '47', '56,383', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '56,384', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '56,401', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '47', '56,417', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '56,825', 'INFO', 'Preprocessing pipeline created successfully']\n",
      "['2020-07-29 09', '47', '56,826', 'INFO', 'Creating grid variables']\n",
      "['2020-07-29 09', '47', '56,829', 'INFO', 'Creating global containers']\n",
      "['2020-07-29 09', '47', '56,945', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '57,059', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '57,060', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '57,086', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '47', '57,104', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '57,516', 'INFO', 'mlflow==1.8.0']\n",
      "['2020-07-29 09', '47', '57,516', 'INFO', 'Checking Exceptions']\n",
      "['2020-07-29 09', '47', '57,718', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '57,739', 'INFO', 'Logging experiment in MLFlow']\n",
      "['2020-07-29 09', '47', '57,833', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '57,834', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '57,859', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '47', '57,881', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '58,479', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '58,479', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '58,508', 'INFO', 'save_model(model=Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_L...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "['verbose=False), model_name=Transformation Pipeline, verbose=False)']\n",
      "['2020-07-29 09', '47', '58,508', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '58,521', 'INFO', 'Transformation Pipeline.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '58,552', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_L...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "['verbose=False), Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_L...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "['verbose=False)]']\n",
      "['2020-07-29 09', '47', '58,552', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '58,552', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '58,578', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '58,692', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '58,693', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '58,708', 'INFO', 'Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_L...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "['verbose=False)']\n",
      "['2020-07-29 09', '47', '58,708', 'INFO', 'setup() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '58,714', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '47', '58,735', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '47', '59,302', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '47', '59,421', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '47', '59,422', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '47', '59,443', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '47', '59,444', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '47', '59,489', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '47', '59,672', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '47', '59,672', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '47', '59,674', 'INFO', \"save_model(model=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\"]\n",
      "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n",
      "['max_samples=None, min_impurity_decrease=0.0,']\n",
      "['min_impurity_split=None, min_samples_leaf=1,']\n",
      "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n",
      "['n_estimators=100, n_jobs=-1, oob_score=False,']\n",
      "['random_state=123, verbose=0, warm_start=False), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '47', '59,674', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '47', '59,703', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '47', '59,789', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '47', '59,796', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '47', '59,811', 'INFO', 'plot type', 'cluster']\n",
      "['2020-07-29 09', '47', '59,812', 'INFO', 'SubProcess assign_model() called ==================================']\n",
      "['2020-07-29 09', '47', '59,812', 'INFO', 'Initializing assign_model()']\n",
      "['2020-07-29 09', '47', '59,812', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "[\"verbose=False), RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\"]\n",
      "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n",
      "['max_samples=None, min_impurity_decrease=0.0,']\n",
      "['min_impurity_split=None, min_samples_leaf=1,']\n",
      "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n",
      "['n_estimators=100, n_jobs=-1, oob_score=False,']\n",
      "['random_state=123, verbose=0, warm_start=False), None]']\n",
      "['2020-07-29 09', '47', '59,812', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '59,812', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '47', '59,812', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), transformation=True, verbose=False)']\n",
      "['2020-07-29 09', '47', '59,812', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '47', '59,813', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '47', '59,813', 'INFO', 'Copying data']\n",
      "['2020-07-29 09', '47', '59,813', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n",
      "['2020-07-29 09', '47', '59,814', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '47', '59,849', 'INFO', 'Determining Trained Model']\n",
      "['2020-07-29 09', '47', '59,850', 'INFO', 'Trained Model', 'K-Means Clustering']\n",
      "['2020-07-29 09', '47', '59,850', 'INFO', '(224, 21)']\n",
      "['2020-07-29 09', '47', '59,851', 'INFO', 'assign_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '47', '59,851', 'INFO', 'SubProcess assign_model() end ==================================']\n",
      "['2020-07-29 09', '47', '59,858', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '47', '59,859', 'INFO', 'Declaring global variables']\n",
      "['2020-07-29 09', '47', '59,860', 'INFO', 'Input provided', 'dataframe']\n",
      "['2020-07-29 09', '47', '59,860', 'INFO', 'session_id set to', '123']\n",
      "['2020-07-29 09', '47', '59,860', 'INFO', 'Copying training dataset']\n",
      "['2020-07-29 09', '47', '59,864', 'INFO', 'Importing stopwords from nltk']\n",
      "['2020-07-29 09', '47', '59,876', 'INFO', 'Fitting PCA()']\n",
      "['2020-07-29 09', '47', '59,891', 'INFO', 'Sorting dataframe']\n",
      "['2020-07-29 09', '47', '59,897', 'INFO', 'Rendering Visual']\n",
      "['2020-07-29 09', '47', '59,977', 'INFO', 'Initializing Extra Trees Regressor']\n",
      "['2020-07-29 09', '47', '59,992', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '48', '00,014', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '48', '00,280', 'INFO', 'No custom stopwords defined']\n",
      "['2020-07-29 09', '48', '00,282', 'INFO', 'Removing numeric characters from the text']\n",
      "['2020-07-29 09', '48', '00,441', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '48', '00,553', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '48', '00,554', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '48', '00,576', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '48', '00,594', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '48', '00,820', 'INFO', 'Removing special characters from the text']\n",
      "['2020-07-29 09', '48', '01,075', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '48', '01,191', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '48', '01,192', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '48', '01,219', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '48', '01,241', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '48', '01,696', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '48', '01,807', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '48', '01,807', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '48', '01,823', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '48', '01,836', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '48', '02,159', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '48', '02,269', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '48', '02,270', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '48', '02,285', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '48', '02,305', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '48', '02,353', 'INFO', 'Initializing create_model()']\n",
      "['2020-07-29 09', '48', '02,353', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '02,353', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '48', '02,354', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '48', '02,421', 'INFO', 'Importing untrained model']\n",
      "['2020-07-29 09', '48', '02,422', 'INFO', 'Isolation Forest Imported succesfully']\n",
      "['2020-07-29 09', '48', '02,449', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '48', '02,813', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '48', '02,928', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '48', '02,929', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '48', '02,953', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '48', '02,954', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '48', '03,011', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '48', '03,241', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '48', '03,242', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '48', '03,244', 'INFO', \"save_model(model=ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\"]\n",
      "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n",
      "['max_samples=None, min_impurity_decrease=0.0,']\n",
      "['min_impurity_split=None, min_samples_leaf=1,']\n",
      "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n",
      "['n_estimators=100, n_jobs=-1, oob_score=False,']\n",
      "['random_state=123, verbose=0, warm_start=False), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '48', '03,244', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '48', '03,413', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '48', '03,441', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "[\"verbose=False), ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\"]\n",
      "[\"max_depth=None, max_features='auto', max_leaf_nodes=None,\"]\n",
      "['max_samples=None, min_impurity_decrease=0.0,']\n",
      "['min_impurity_split=None, min_samples_leaf=1,']\n",
      "['min_samples_split=2, min_weight_fraction_leaf=0.0,']\n",
      "['n_estimators=100, n_jobs=-1, oob_score=False,']\n",
      "['random_state=123, verbose=0, warm_start=False), None]']\n",
      "['2020-07-29 09', '48', '03,441', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '03,442', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '48', '03,744', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '48', '03,753', 'INFO', 'Initializing AdaBoost Regressor']\n",
      "['2020-07-29 09', '48', '03,780', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '48', '03,810', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '48', '03,946', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '48', '03,960', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '48', '03,961', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '48', '03,961', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '48', '03,961', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '48', '03,962', 'INFO', \"save_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "['random_state=123, verbose=0), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '48', '03,962', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '48', '03,999', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '48', '04,028', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '48', '04,118', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '48', '04,126', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '48', '04,127', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '48', '04,146', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '48', '04,165', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '48', '04,169', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '48', '04,188', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_L...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "[\"verbose=False), IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "['random_state=123, verbose=0)]']\n",
      "['2020-07-29 09', '48', '04,188', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '04,189', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '48', '04,228', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '48', '04,238', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '48', '04,238', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '48', '04,265', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '48', '04,290', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '48', '04,301', 'INFO', \"IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "['random_state=123, verbose=0)']\n",
      "['2020-07-29 09', '48', '04,301', 'INFO', 'create_models() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '04,317', 'INFO', 'Initializing create_model()']\n",
      "['2020-07-29 09', '48', '04,318', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '04,318', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '48', '04,318', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '48', '04,350', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '48', '04,360', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '48', '04,360', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '48', '04,389', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '48', '04,395', 'INFO', 'Importing untrained model']\n",
      "['2020-07-29 09', '48', '04,396', 'INFO', 'k-Nearest Neighbors Detector Imported succesfully']\n",
      "['2020-07-29 09', '48', '04,412', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '48', '04,415', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '48', '04,484', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '48', '04,484', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '48', '04,494', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '48', '04,494', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '48', '04,518', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '48', '04,519', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '48', '04,583', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '48', '04,697', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '48', '04,698', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '48', '04,699', 'INFO', \"save_model(model=KNN(algorithm='auto', contamination=0.1, leaf_size=30, method='largest',\"]\n",
      "[\"metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=5, p=2,\"]\n",
      "['radius=1.0), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '48', '04,700', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '48', '04,725', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '48', '04,747', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_L...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "[\"verbose=False), KNN(algorithm='auto', contamination=0.1, leaf_size=30, method='largest',\"]\n",
      "[\"metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=5, p=2,\"]\n",
      "['radius=1.0)]']\n",
      "['2020-07-29 09', '48', '04,747', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '04,748', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '48', '04,794', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '48', '04,794', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '48', '04,795', 'INFO', \"save_model(model=AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\"]\n",
      "['n_estimators=50, random_state=123), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '48', '04,796', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '48', '04,831', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '48', '04,855', 'INFO', \"KNN(algorithm='auto', contamination=0.1, leaf_size=30, method='largest',\"]\n",
      "[\"metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=5, p=2,\"]\n",
      "['radius=1.0)']\n",
      "['2020-07-29 09', '48', '04,855', 'INFO', 'create_models() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '04,856', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "[\"verbose=False), AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\"]\n",
      "['n_estimators=50, random_state=123), None]']\n",
      "['2020-07-29 09', '48', '04,856', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '04,856', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '48', '04,871', 'INFO', 'Initializing assign_model()']\n",
      "['2020-07-29 09', '48', '04,873', 'INFO', \"assign_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "['random_state=123, verbose=0), transformation=False, score=True, verbose=True)']\n",
      "['2020-07-29 09', '48', '04,873', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '48', '04,874', 'INFO', 'Copying data']\n",
      "['2020-07-29 09', '48', '04,876', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '48', '04,952', 'INFO', 'Determining Trained Model']\n",
      "['2020-07-29 09', '48', '04,953', 'INFO', 'Trained Model', 'Assigned Isolation Forest']\n",
      "['2020-07-29 09', '48', '04,956', 'INFO', '(1000, 12)']\n",
      "['2020-07-29 09', '48', '04,957', 'INFO', 'assign_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '05,009', 'INFO', 'Initializing plot_model()']\n",
      "['2020-07-29 09', '48', '05,010', 'INFO', \"plot_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "['random_state=123, verbose=0), plot=tsne, feature=None, save=False, system=True)']\n",
      "['2020-07-29 09', '48', '05,011', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '05,011', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '48', '05,796', 'INFO', 'Tokenizing Words']\n",
      "['2020-07-29 09', '48', '06,772', 'INFO', \"Saving 'Cluster.html' in current active directory\"]\n",
      "['2020-07-29 09', '48', '06,773', 'INFO', 'Visual Rendered Successfully']\n",
      "['2020-07-29 09', '48', '06,773', 'INFO', 'plot_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '07,650', 'INFO', 'Initializing plot_model()']\n",
      "['2020-07-29 09', '48', '07,651', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), plot=distribution, feature=None, label=False, save=True, system=False)']\n",
      "['2020-07-29 09', '48', '07,652', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '07,652', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '48', '07,672', 'INFO', 'plot type', 'distribution']\n",
      "['2020-07-29 09', '48', '07,672', 'INFO', 'SubProcess assign_model() called ==================================']\n",
      "['2020-07-29 09', '48', '07,672', 'INFO', 'Initializing assign_model()']\n",
      "['2020-07-29 09', '48', '07,674', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), transformation=False, verbose=False)']\n",
      "['2020-07-29 09', '48', '07,674', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '07,675', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '48', '07,675', 'INFO', 'Copying data']\n",
      "['2020-07-29 09', '48', '07,676', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '48', '07,711', 'INFO', 'Determining Trained Model']\n",
      "['2020-07-29 09', '48', '07,713', 'INFO', 'Trained Model', 'K-Means Clustering']\n",
      "['2020-07-29 09', '48', '07,714', 'INFO', '(224, 22)']\n",
      "['2020-07-29 09', '48', '07,714', 'INFO', 'assign_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '07,715', 'INFO', 'SubProcess assign_model() end ==================================']\n",
      "['2020-07-29 09', '48', '07,715', 'INFO', 'Sorting dataframe']\n",
      "['2020-07-29 09', '48', '07,732', 'INFO', 'Rendering Visual']\n",
      "['2020-07-29 09', '48', '08,329', 'INFO', \"Saving 'Distribution.html' in current active directory\"]\n",
      "['2020-07-29 09', '48', '08,329', 'INFO', 'Visual Rendered Successfully']\n",
      "['2020-07-29 09', '48', '08,329', 'INFO', 'plot_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '08,812', 'INFO', 'plot type', 'tsne']\n",
      "['2020-07-29 09', '48', '08,813', 'INFO', 'SubProcess assign_model() called ==================================']\n",
      "['2020-07-29 09', '48', '08,813', 'INFO', 'Initializing assign_model()']\n",
      "['2020-07-29 09', '48', '08,814', 'INFO', \"assign_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "['random_state=123, verbose=0), transformation=True, score=False, verbose=False)']\n",
      "['2020-07-29 09', '48', '08,814', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '48', '08,815', 'INFO', 'Copying data']\n",
      "['2020-07-29 09', '48', '08,815', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '48', '08,849', 'INFO', 'Determining Trained Model']\n",
      "['2020-07-29 09', '48', '08,850', 'INFO', 'Trained Model', 'Assigned Isolation Forest']\n",
      "['2020-07-29 09', '48', '08,851', 'INFO', '(1000, 11)']\n",
      "['2020-07-29 09', '48', '08,851', 'INFO', 'assign_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '08,852', 'INFO', 'SubProcess assign_model() end ==================================']\n",
      "['2020-07-29 09', '48', '08,870', 'INFO', 'Getting dummies to cast categorical variables']\n",
      "['2020-07-29 09', '48', '08,885', 'INFO', 'Fitting TSNE()']\n",
      "['2020-07-29 09', '48', '09,534', 'INFO', 'Initializing plot_model()']\n",
      "['2020-07-29 09', '48', '09,535', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), plot=elbow, feature=None, label=False, save=True, system=False)']\n",
      "['2020-07-29 09', '48', '09,535', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '09,536', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '48', '09,562', 'INFO', 'plot type', 'elbow']\n",
      "['2020-07-29 09', '48', '09,834', 'INFO', 'Fitting KElbowVisualizer()']\n",
      "['2020-07-29 09', '48', '11,999', 'INFO', 'Rendering Visual']\n",
      "['2020-07-29 09', '48', '13,890', 'INFO', \"Saving 'Elbow.png' in current active directory\"]\n",
      "['2020-07-29 09', '48', '13,890', 'INFO', 'Visual Rendered Successfully']\n",
      "['2020-07-29 09', '48', '13,891', 'INFO', 'plot_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '13,931', 'INFO', 'SubProcess plot_model() end ==================================']\n",
      "['2020-07-29 09', '48', '13,932', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '48', '13,932', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '48', '13,934', 'INFO', \"save_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '48', '13,934', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '48', '13,953', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '48', '13,975', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True,']\n",
      "[\"features_todrop=['Country Name'],\"]\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "[\"verbose=False), KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0)]']\n",
      "['2020-07-29 09', '48', '13,975', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '13,976', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '48', '14,077', 'INFO', \"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0)']\n",
      "['2020-07-29 09', '48', '14,078', 'INFO', 'create_models() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '14,099', 'INFO', 'Initializing create_model()']\n",
      "['2020-07-29 09', '48', '14,100', 'INFO', 'create_model(model=kmodes, num_clusters=4, ground_truth=None, verbose=True, system=True)']\n",
      "['2020-07-29 09', '48', '14,100', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '14,101', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '48', '14,102', 'INFO', 'Setting num_cluster param']\n",
      "['2020-07-29 09', '48', '14,102', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '48', '14,162', 'INFO', 'Importing untrained model']\n",
      "['2020-07-29 09', '48', '14,178', 'INFO', 'K-Modes Clustering Imported succesfully']\n",
      "['2020-07-29 09', '48', '14,201', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '48', '20,873', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '48', '20,911', 'INFO', 'Creating Metrics dataframe']\n",
      "['2020-07-29 09', '48', '20,919', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '48', '21,190', 'INFO', 'SubProcess plot_model() called ==================================']\n",
      "['2020-07-29 09', '48', '21,190', 'INFO', 'Initializing plot_model()']\n",
      "['2020-07-29 09', '48', '21,192', 'INFO', \"plot_model(model=KModes(cat_dissim=<function matching_dissim at 0x00000229DC93C730>, init='Cao',\"]\n",
      "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n",
      "['verbose=0), plot=cluster, feature=None, label=False, save=True, system=False)']\n",
      "['2020-07-29 09', '48', '21,192', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '21,192', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '48', '21,221', 'INFO', 'plot type', 'cluster']\n",
      "['2020-07-29 09', '48', '21,221', 'INFO', 'SubProcess assign_model() called ==================================']\n",
      "['2020-07-29 09', '48', '21,221', 'INFO', 'Initializing assign_model()']\n",
      "['2020-07-29 09', '48', '21,223', 'INFO', \"assign_model(model=KModes(cat_dissim=<function matching_dissim at 0x00000229DC93C730>, init='Cao',\"]\n",
      "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n",
      "['verbose=0), transformation=True, verbose=False)']\n",
      "['2020-07-29 09', '48', '21,223', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '21,223', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '48', '21,224', 'INFO', 'Copying data']\n",
      "['2020-07-29 09', '48', '21,224', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n",
      "['2020-07-29 09', '48', '21,225', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '48', '21,271', 'INFO', 'Determining Trained Model']\n",
      "['2020-07-29 09', '48', '21,273', 'INFO', 'Trained Model', 'K-Modes Clustering']\n",
      "['2020-07-29 09', '48', '21,274', 'INFO', '(224, 21)']\n",
      "['2020-07-29 09', '48', '21,274', 'INFO', 'assign_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '21,275', 'INFO', 'SubProcess assign_model() end ==================================']\n",
      "['2020-07-29 09', '48', '21,292', 'INFO', 'Fitting PCA()']\n",
      "['2020-07-29 09', '48', '21,315', 'INFO', 'Sorting dataframe']\n",
      "['2020-07-29 09', '48', '21,323', 'INFO', 'Rendering Visual']\n",
      "['2020-07-29 09', '48', '21,784', 'INFO', \"Saving 'Cluster.html' in current active directory\"]\n",
      "['2020-07-29 09', '48', '21,784', 'INFO', 'Visual Rendered Successfully']\n",
      "['2020-07-29 09', '48', '21,785', 'INFO', 'plot_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '21,905', 'INFO', 'Removing stopwords']\n",
      "['2020-07-29 09', '48', '23,356', 'INFO', 'Initializing plot_model()']\n",
      "['2020-07-29 09', '48', '23,357', 'INFO', \"plot_model(model=KModes(cat_dissim=<function matching_dissim at 0x00000229DC93C730>, init='Cao',\"]\n",
      "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n",
      "['verbose=0), plot=distribution, feature=None, label=False, save=True, system=False)']\n",
      "['2020-07-29 09', '48', '23,357', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '23,357', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '48', '23,386', 'INFO', 'plot type', 'distribution']\n",
      "['2020-07-29 09', '48', '23,387', 'INFO', 'SubProcess assign_model() called ==================================']\n",
      "['2020-07-29 09', '48', '23,387', 'INFO', 'Initializing assign_model()']\n",
      "['2020-07-29 09', '48', '23,388', 'INFO', \"assign_model(model=KModes(cat_dissim=<function matching_dissim at 0x00000229DC93C730>, init='Cao',\"]\n",
      "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n",
      "['verbose=0), transformation=False, verbose=False)']\n",
      "['2020-07-29 09', '48', '23,388', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '23,388', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '48', '23,388', 'INFO', 'Copying data']\n",
      "['2020-07-29 09', '48', '23,391', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '48', '23,427', 'INFO', 'Determining Trained Model']\n",
      "['2020-07-29 09', '48', '23,428', 'INFO', 'Trained Model', 'K-Modes Clustering']\n",
      "['2020-07-29 09', '48', '23,429', 'INFO', '(224, 22)']\n",
      "['2020-07-29 09', '48', '23,429', 'INFO', 'assign_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '23,429', 'INFO', 'SubProcess assign_model() end ==================================']\n",
      "['2020-07-29 09', '48', '23,430', 'INFO', 'Sorting dataframe']\n",
      "['2020-07-29 09', '48', '23,439', 'INFO', 'Rendering Visual']\n",
      "['2020-07-29 09', '48', '24,069', 'INFO', \"Saving 'Distribution.html' in current active directory\"]\n",
      "['2020-07-29 09', '48', '24,069', 'INFO', 'Visual Rendered Successfully']\n",
      "['2020-07-29 09', '48', '24,070', 'INFO', 'plot_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '25,580', 'INFO', 'Initializing plot_model()']\n",
      "['2020-07-29 09', '48', '25,582', 'INFO', \"plot_model(model=KModes(cat_dissim=<function matching_dissim at 0x00000229DC93C730>, init='Cao',\"]\n",
      "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n",
      "['verbose=0), plot=elbow, feature=None, label=False, save=True, system=False)']\n",
      "['2020-07-29 09', '48', '25,582', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '25,582', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '48', '25,604', 'INFO', 'plot type', 'elbow']\n",
      "['2020-07-29 09', '48', '25,658', 'INFO', 'Fitting KElbowVisualizer()']\n",
      "['2020-07-29 09', '48', '32,253', 'INFO', 'Extracting Bigrams']\n",
      "['2020-07-29 09', '48', '41,062', 'INFO', 'Rendering Visual']\n",
      "['2020-07-29 09', '48', '41,646', 'INFO', \"Saving 'Elbow.png' in current active directory\"]\n",
      "['2020-07-29 09', '48', '41,646', 'INFO', 'Visual Rendered Successfully']\n",
      "['2020-07-29 09', '48', '41,646', 'INFO', 'plot_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '41,692', 'INFO', 'SubProcess plot_model() end ==================================']\n",
      "['2020-07-29 09', '48', '41,692', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '48', '41,693', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '48', '41,694', 'INFO', \"save_model(model=KModes(cat_dissim=<function matching_dissim at 0x00000229DC93C730>, init='Cao',\"]\n",
      "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n",
      "['verbose=0), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '48', '41,694', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '48', '42,305', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '48', '42,330', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True,']\n",
      "[\"features_todrop=['Country Name'],\"]\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "[\"verbose=False), KModes(cat_dissim=<function matching_dissim at 0x00000229DC93C730>, init='Cao',\"]\n",
      "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n",
      "['verbose=0)]']\n",
      "['2020-07-29 09', '48', '42,330', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '42,330', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '48', '42,451', 'INFO', \"KModes(cat_dissim=<function matching_dissim at 0x00000229DC93C730>, init='Cao',\"]\n",
      "['max_iter=100, n_clusters=4, n_init=1, n_jobs=-1, random_state=123,']\n",
      "['verbose=0)']\n",
      "['2020-07-29 09', '48', '42,451', 'INFO', 'create_models() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '42,468', 'INFO', 'Initializing assign_model()']\n",
      "['2020-07-29 09', '48', '42,469', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), transformation=False, verbose=True)']\n",
      "['2020-07-29 09', '48', '42,470', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '42,470', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '48', '42,470', 'INFO', 'Copying data']\n",
      "['2020-07-29 09', '48', '42,472', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '48', '42,563', 'INFO', 'Determining Trained Model']\n",
      "['2020-07-29 09', '48', '42,573', 'INFO', 'Trained Model', 'K-Means Clustering']\n",
      "['2020-07-29 09', '48', '42,576', 'INFO', '(224, 22)']\n",
      "['2020-07-29 09', '48', '42,577', 'INFO', 'assign_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '42,694', 'INFO', 'Initializing plot_model()']\n",
      "['2020-07-29 09', '48', '42,695', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), plot=cluster, feature=None, label=False, save=False, system=True)']\n",
      "['2020-07-29 09', '48', '42,696', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '42,696', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '48', '42,737', 'INFO', 'plot type', 'cluster']\n",
      "['2020-07-29 09', '48', '42,737', 'INFO', 'SubProcess assign_model() called ==================================']\n",
      "['2020-07-29 09', '48', '42,738', 'INFO', 'Initializing assign_model()']\n",
      "['2020-07-29 09', '48', '42,739', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), transformation=True, verbose=False)']\n",
      "['2020-07-29 09', '48', '42,740', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '42,740', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '48', '42,740', 'INFO', 'Copying data']\n",
      "['2020-07-29 09', '48', '42,741', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n",
      "['2020-07-29 09', '48', '42,741', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '48', '42,782', 'INFO', 'Determining Trained Model']\n",
      "['2020-07-29 09', '48', '42,788', 'INFO', 'Trained Model', 'K-Means Clustering']\n",
      "['2020-07-29 09', '48', '42,789', 'INFO', '(224, 21)']\n",
      "['2020-07-29 09', '48', '42,789', 'INFO', 'assign_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '42,790', 'INFO', 'SubProcess assign_model() end ==================================']\n",
      "['2020-07-29 09', '48', '42,805', 'INFO', 'Fitting PCA()']\n",
      "['2020-07-29 09', '48', '42,824', 'INFO', 'Sorting dataframe']\n",
      "['2020-07-29 09', '48', '42,833', 'INFO', 'Rendering Visual']\n",
      "['2020-07-29 09', '48', '43,140', 'INFO', 'Visual Rendered Successfully']\n",
      "['2020-07-29 09', '48', '43,140', 'INFO', 'plot_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '43,154', 'INFO', 'Initializing plot_model()']\n",
      "['2020-07-29 09', '48', '43,156', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), plot=cluster, feature=Country Name, label=True, save=False, system=True)']\n",
      "['2020-07-29 09', '48', '43,156', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '43,157', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '48', '43,186', 'INFO', 'plot type', 'cluster']\n",
      "['2020-07-29 09', '48', '43,188', 'INFO', 'SubProcess assign_model() called ==================================']\n",
      "['2020-07-29 09', '48', '43,188', 'INFO', 'Initializing assign_model()']\n",
      "['2020-07-29 09', '48', '43,192', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), transformation=True, verbose=False)']\n",
      "['2020-07-29 09', '48', '43,192', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '43,193', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '48', '43,193', 'INFO', 'Copying data']\n",
      "['2020-07-29 09', '48', '43,194', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n",
      "['2020-07-29 09', '48', '43,194', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '48', '43,230', 'INFO', 'Determining Trained Model']\n",
      "['2020-07-29 09', '48', '43,232', 'INFO', 'Trained Model', 'K-Means Clustering']\n",
      "['2020-07-29 09', '48', '43,233', 'INFO', '(224, 21)']\n",
      "['2020-07-29 09', '48', '43,233', 'INFO', 'assign_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '43,233', 'INFO', 'SubProcess assign_model() end ==================================']\n",
      "['2020-07-29 09', '48', '43,246', 'INFO', 'Fitting PCA()']\n",
      "['2020-07-29 09', '48', '43,268', 'INFO', 'Sorting dataframe']\n",
      "['2020-07-29 09', '48', '43,274', 'INFO', 'Rendering Visual']\n",
      "['2020-07-29 09', '48', '43,585', 'INFO', 'Visual Rendered Successfully']\n",
      "['2020-07-29 09', '48', '43,585', 'INFO', 'plot_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '43,599', 'INFO', 'Initializing plot_model()']\n",
      "['2020-07-29 09', '48', '43,601', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), plot=tsne, feature=None, label=False, save=False, system=True)']\n",
      "['2020-07-29 09', '48', '43,602', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '43,602', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '48', '43,658', 'INFO', 'plot type', 'tsne']\n",
      "['2020-07-29 09', '48', '43,659', 'INFO', 'SubProcess assign_model() called ==================================']\n",
      "['2020-07-29 09', '48', '43,659', 'INFO', 'Initializing assign_model()']\n",
      "['2020-07-29 09', '48', '43,661', 'INFO', \"assign_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), transformation=True, verbose=False)']\n",
      "['2020-07-29 09', '48', '43,662', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '43,662', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '48', '43,663', 'INFO', 'Copying data']\n",
      "['2020-07-29 09', '48', '43,664', 'INFO', 'Transformation param set to True. Assigned clusters are attached on transformed dataset.']\n",
      "['2020-07-29 09', '48', '43,664', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '48', '43,711', 'INFO', 'Determining Trained Model']\n",
      "['2020-07-29 09', '48', '43,713', 'INFO', 'Trained Model', 'K-Means Clustering']\n",
      "['2020-07-29 09', '48', '43,714', 'INFO', '(224, 21)']\n",
      "['2020-07-29 09', '48', '43,714', 'INFO', 'assign_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '43,714', 'INFO', 'SubProcess assign_model() end ==================================']\n",
      "['2020-07-29 09', '48', '43,718', 'INFO', 'Fitting TSNE()']\n",
      "['2020-07-29 09', '48', '49,461', 'INFO', 'Rendering Visual']\n",
      "['2020-07-29 09', '48', '59,261', 'INFO', 'Sorting dataframe']\n",
      "['2020-07-29 09', '48', '59,268', 'INFO', 'Rendering Visual']\n",
      "['2020-07-29 09', '48', '59,625', 'INFO', 'Visual Rendered Successfully']\n",
      "['2020-07-29 09', '48', '59,626', 'INFO', 'plot_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '48', '59,656', 'INFO', 'Initializing plot_model()']\n",
      "['2020-07-29 09', '48', '59,658', 'INFO', \"plot_model(model=KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\"]\n",
      "[\"n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\"]\n",
      "['random_state=123, tol=0.0001, verbose=0), plot=elbow, feature=None, label=False, save=False, system=True)']\n",
      "['2020-07-29 09', '48', '59,658', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '48', '59,658', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '48', '59,688', 'INFO', 'plot type', 'elbow']\n",
      "['2020-07-29 09', '48', '59,688', 'INFO', 'Fitting KElbowVisualizer()']\n",
      "['2020-07-29 09', '49', '01,501', 'INFO', 'Visual Rendered Successfully']\n",
      "['2020-07-29 09', '49', '01,501', 'INFO', 'plot_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '01,532', 'INFO', 'Initializing plot_model()']\n",
      "['2020-07-29 09', '49', '01,533', 'INFO', \"plot_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "['random_state=123, verbose=0), plot=umap, feature=None, save=False, system=True)']\n",
      "['2020-07-29 09', '49', '01,533', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '49', '01,533', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '49', '01,555', 'INFO', 'plot type', 'umap']\n",
      "['2020-07-29 09', '49', '01,556', 'INFO', 'SubProcess assign_model() called ==================================']\n",
      "['2020-07-29 09', '49', '01,556', 'INFO', 'Initializing assign_model()']\n",
      "['2020-07-29 09', '49', '01,556', 'INFO', \"assign_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "['random_state=123, verbose=0), transformation=True, score=False, verbose=False)']\n",
      "['2020-07-29 09', '49', '01,557', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '49', '01,557', 'INFO', 'Copying data']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2020-07-29 09', '49', '01,558', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '49', '01,588', 'INFO', 'Determining Trained Model']\n",
      "['2020-07-29 09', '49', '01,589', 'INFO', 'Trained Model', 'Assigned Isolation Forest']\n",
      "['2020-07-29 09', '49', '01,590', 'INFO', '(1000, 11)']\n",
      "['2020-07-29 09', '49', '01,590', 'INFO', 'assign_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '01,590', 'INFO', 'SubProcess assign_model() end ==================================']\n",
      "['2020-07-29 09', '49', '01,598', 'INFO', 'Getting dummies to cast categorical variables']\n",
      "['2020-07-29 09', '49', '02,407', 'INFO', 'Extracting Trigrams']\n",
      "['2020-07-29 09', '49', '05,305', 'INFO', 'Fitting UMAP()']\n",
      "['2020-07-29 09', '49', '14,048', 'INFO', 'PyCaret Regression Module']\n",
      "['2020-07-29 09', '49', '14,048', 'INFO', 'version pycaret-nightly-0.39']\n",
      "['2020-07-29 09', '49', '14,048', 'INFO', 'Initializing setup()']\n",
      "['2020-07-29 09', '49', '14,049', 'INFO', 'USI', 'd354']\n",
      "['2020-07-29 09', '49', '14,049', 'INFO', 'setup(data=(1338, 7), target=charges, train_size=0.7, sampling=True, sample_estimator=None, categorical_features=None, categorical_imputation=constant, ordinal_features=None,']\n",
      "['high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, numeric_imputation=mean, date_features=None, ignore_features=None, normalize=False,']\n",
      "['normalize_method=zscore, transformation=False, transformation_method=yeo-johnson, handle_unknown_categorical=True, unknown_categorical_method=least_frequent, pca=False, pca_method=linear,']\n",
      "['pca_components=None, ignore_low_variance=False, combine_rare_levels=False, rare_level_threshold=0.1, bin_numeric_features=None, remove_outliers=False, outliers_threshold=0.05,']\n",
      "['remove_multicollinearity=False, multicollinearity_threshold=0.9, remove_perfect_collinearity=False, create_clusters=False, cluster_iter=20,']\n",
      "['polynomial_features=False, polynomial_degree=2, trigonometry_features=False, polynomial_threshold=0.1, group_features=None,']\n",
      "['group_names=None, feature_selection=False, feature_selection_threshold=0.8, feature_interaction=False, feature_ratio=False, interaction_threshold=0.01, transform_target=False,']\n",
      "['transform_target_method=box-cox, data_split_shuffle=True, folds_shuffle=False, n_jobs=-1, html=True, session_id=123, log_experiment=True,']\n",
      "['experiment_name=insurance1, log_plots=False, log_profile=False, log_data=False, silent=False, verbose=True, profile=False)']\n",
      "['2020-07-29 09', '49', '14,049', 'INFO', 'Checking environment']\n",
      "['2020-07-29 09', '49', '14,049', 'INFO', 'python_version', '3.6.10']\n",
      "['2020-07-29 09', '49', '14,050', 'INFO', 'python_build', \"('default', 'May  7 2020 19\", '46', \"08')\"]\n",
      "['2020-07-29 09', '49', '14,050', 'INFO', 'machine', 'AMD64']\n",
      "['2020-07-29 09', '49', '14,050', 'INFO', 'platform', 'Windows-10-10.0.18362-SP0']\n",
      "['2020-07-29 09', '49', '14,097', 'INFO', 'Memory', 'svmem(total=17032478720, available=5629382656, percent=66.9, used=11403096064, free=5629382656)']\n",
      "['2020-07-29 09', '49', '14,097', 'INFO', 'Physical Core', '4']\n",
      "['2020-07-29 09', '49', '14,097', 'INFO', 'Logical Core', '8']\n",
      "['2020-07-29 09', '49', '14,097', 'INFO', 'Checking libraries']\n",
      "['2020-07-29 09', '49', '14,097', 'INFO', 'pd==1.0.4']\n",
      "['2020-07-29 09', '49', '14,098', 'INFO', 'numpy==1.18.5']\n",
      "['2020-07-29 09', '49', '14,935', 'INFO', 'sklearn==0.23.1']\n",
      "['2020-07-29 09', '49', '15,080', 'INFO', 'xgboost==1.1.1']\n",
      "['2020-07-29 09', '49', '15,220', 'INFO', 'lightgbm==2.3.1']\n",
      "['2020-07-29 09', '49', '15,339', 'INFO', 'catboost==0.23.2']\n",
      "['2020-07-29 09', '49', '16,374', 'INFO', 'mlflow==1.8.0']\n",
      "['2020-07-29 09', '49', '16,375', 'INFO', 'Checking Exceptions']\n",
      "['2020-07-29 09', '49', '16,375', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '49', '16,375', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '49', '16,408', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '49', '19,757', 'INFO', 'Copying data for preprocessing']\n",
      "['2020-07-29 09', '49', '19,758', 'INFO', 'Declaring global variables']\n",
      "['2020-07-29 09', '49', '19,777', 'INFO', 'Declaring preprocessing parameters']\n",
      "['2020-07-29 09', '49', '19,777', 'INFO', 'Importing preprocessing module']\n",
      "['2020-07-29 09', '49', '21,005', 'INFO', 'Creating preprocessing pipeline']\n",
      "['2020-07-29 09', '49', '22,813', 'INFO', 'Preprocessing pipeline created successfully']\n",
      "['2020-07-29 09', '49', '22,813', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '22,814', 'INFO', 'Creating grid variables']\n",
      "['2020-07-29 09', '49', '22,818', 'INFO', 'Creating global containers']\n",
      "['2020-07-29 09', '49', '22,997', 'INFO', 'Logging experiment in MLFlow']\n",
      "['2020-07-29 09', '49', '23,497', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '49', '23,497', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '49', '23,514', 'INFO', 'save_model(model=Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), model_name=Transformation Pipeline, verbose=False)']\n",
      "['2020-07-29 09', '49', '23,514', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '49', '23,528', 'INFO', 'Transformation Pipeline.pkl saved in current working directory']\n",
      "['2020-07-29 09', '49', '23,556', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), None]']\n",
      "['2020-07-29 09', '49', '23,557', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '23,557', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '49', '24,045', 'INFO', 'create_model_container', '0']\n",
      "['2020-07-29 09', '49', '24,045', 'INFO', 'master_model_container', '0']\n",
      "['2020-07-29 09', '49', '24,045', 'INFO', 'display_container', '0']\n",
      "['2020-07-29 09', '49', '24,045', 'INFO', 'setup() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '25,845', 'INFO', 'Initializing compare_models()']\n",
      "['2020-07-29 09', '49', '25,845', 'INFO', 'compare_models(blacklist=None, whitelist=None, fold=5, round=4, sort=R2, n_select=1, turbo=True, verbose=True)']\n",
      "['2020-07-29 09', '49', '25,845', 'INFO', 'Checking exceptions']\n",
      "['2020-07-29 09', '49', '25,846', 'INFO', 'Preloading libraries']\n",
      "['2020-07-29 09', '49', '25,846', 'INFO', 'Preparing display monitor']\n",
      "['2020-07-29 09', '49', '25,953', 'INFO', 'Copying training dataset']\n",
      "['2020-07-29 09', '49', '25,957', 'INFO', 'Importing libraries']\n",
      "['2020-07-29 09', '49', '25,997', 'INFO', 'Importing untrained models']\n",
      "['2020-07-29 09', '49', '26,001', 'INFO', 'Import successful']\n",
      "['2020-07-29 09', '49', '26,023', 'INFO', 'Defining folds']\n",
      "['2020-07-29 09', '49', '26,023', 'INFO', 'Declaring metric variables']\n",
      "['2020-07-29 09', '49', '26,023', 'INFO', 'Initializing Linear Regression']\n",
      "['2020-07-29 09', '49', '26,050', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '49', '26,072', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '26,081', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '26,097', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '26,098', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '26,243', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '49', '26,280', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '26,291', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '26,298', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '26,298', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '26,372', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '49', '26,407', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '26,424', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '26,430', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '26,430', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '26,489', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '49', '26,529', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '26,542', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '26,547', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '26,547', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '26,627', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '49', '26,685', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '26,695', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '26,701', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '26,702', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '26,784', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '49', '26,787', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '49', '26,947', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '49', '27,492', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '49', '27,492', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '49', '27,496', 'INFO', 'save_model(model=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '49', '27,496', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '49', '27,584', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '49', '27,637', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False), None]']\n",
      "['2020-07-29 09', '49', '27,638', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '27,638', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '49', '27,641', 'INFO', 'Rendering Visual']\n",
      "['2020-07-29 09', '49', '27,775', 'INFO', 'Initializing Lasso Regression']\n",
      "['2020-07-29 09', '49', '27,790', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '49', '27,809', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '27,819', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '27,824', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '27,824', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '27,842', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '49', '27,862', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '27,871', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '27,876', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '27,876', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '27,899', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '49', '27,909', 'INFO', 'Visual Rendered Successfully']\n",
      "['2020-07-29 09', '49', '27,910', 'INFO', 'plot_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '27,921', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '27,931', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '27,939', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '27,939', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '27,968', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '49', '27,993', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '28,006', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '28,013', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '28,014', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '28,044', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '49', '28,072', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '28,083', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '28,091', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '28,091', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '28,118', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '49', '28,119', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '49', '28,162', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '49', '28,366', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '49', '28,367', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '49', '28,367', 'INFO', 'save_model(model=Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,']\n",
      "['normalize=False, positive=False, precompute=False, random_state=123,']\n",
      "[\"selection='cyclic', tol=0.0001, warm_start=False), model_name=Trained Model, verbose=False)\"]\n",
      "['2020-07-29 09', '49', '28,368', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '49', '28,392', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '49', '28,410', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,']\n",
      "['normalize=False, positive=False, precompute=False, random_state=123,']\n",
      "[\"selection='cyclic', tol=0.0001, warm_start=False), None]\"]\n",
      "['2020-07-29 09', '49', '28,410', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '28,410', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '49', '28,583', 'INFO', 'Initializing Ridge Regression']\n",
      "['2020-07-29 09', '49', '28,598', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '49', '28,599', 'INFO', \"save_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "['random_state=123, verbose=0), model_name=iforest, verbose=True)']\n",
      "['2020-07-29 09', '49', '28,600', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '49', '28,607', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '49', '28,630', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '28,638', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '28,645', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '28,645', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '28,663', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '49', '28,682', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '28,692', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '28,697', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '28,697', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '28,720', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '49', '28,744', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '28,752', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '28,758', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '28,759', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '28,766', 'INFO', 'iforest.pkl saved in current working directory']\n",
      "['2020-07-29 09', '49', '28,782', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '49', '28,787', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_L...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "[\"verbose=False), IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "['random_state=123, verbose=0)]']\n",
      "['2020-07-29 09', '49', '28,788', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '28,808', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '28,818', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '28,826', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '28,826', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '28,853', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '49', '28,878', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '28,886', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '28,893', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '28,893', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '28,917', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '49', '28,918', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '49', '28,963', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '49', '29,109', 'INFO', 'Initializing deploy_model()']\n",
      "['2020-07-29 09', '49', '29,111', 'INFO', \"deploy_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "[\"random_state=123, verbose=0), model_name=iforest-aws, authentication={'bucket'\", \"'pycaret-test'}, platform=aws)\"]\n",
      "['2020-07-29 09', '49', '29,111', 'INFO', 'Platform', 'AWS S3']\n",
      "['2020-07-29 09', '49', '29,245', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '49', '29,245', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '49', '29,246', 'INFO', 'save_model(model=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,']\n",
      "[\"normalize=False, random_state=123, solver='auto', tol=0.001), model_name=Trained Model, verbose=False)\"]\n",
      "['2020-07-29 09', '49', '29,247', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '49', '29,272', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '49', '29,288', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,']\n",
      "[\"normalize=False, random_state=123, solver='auto', tol=0.001), None]\"]\n",
      "['2020-07-29 09', '49', '29,289', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '29,289', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '49', '29,429', 'INFO', 'Saving model in current working directory']\n",
      "['2020-07-29 09', '49', '29,430', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '49', '29,430', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '49', '29,430', 'INFO', \"save_model(model=IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "['random_state=123, verbose=0), model_name=iforest-aws, verbose=False)']\n",
      "['2020-07-29 09', '49', '29,431', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '49', '29,481', 'INFO', 'Initializing Elastic Net']\n",
      "['2020-07-29 09', '49', '29,503', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '49', '29,528', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '29,540', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '29,545', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '29,546', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '29,573', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '49', '29,595', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '29,606', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '29,614', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '29,615', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '29,654', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '49', '29,677', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '29,689', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '29,697', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '29,697', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '29,698', 'INFO', 'iforest-aws.pkl saved in current working directory']\n",
      "['2020-07-29 09', '49', '29,722', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "['numerical_features=[],']\n",
      "[\"target='dummy_target',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_L...']\n",
      "[\"target='dummy_target')),\"]\n",
      "[\"('feature_time',\"]\n",
      "['Make_Time_Features(list_of_features=None, time_feature=[])),']\n",
      "[\"('group', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('binn', Empty()),\"]\n",
      "[\"('fix_perfect', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('dummy', Dummify(target='dummy_target')),\"]\n",
      "[\"('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\"]\n",
      "[\"('pca', Empty())],\"]\n",
      "[\"verbose=False), IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "['random_state=123, verbose=0)]']\n",
      "['2020-07-29 09', '49', '29,722', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '29,723', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '49', '29,723', 'INFO', 'Initializing S3 client']\n",
      "['2020-07-29 09', '49', '29,729', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '49', '29,755', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '29,767', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '29,776', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '29,776', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '29,804', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '49', '29,831', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '29,843', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '29,850', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '29,850', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '29,888', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '49', '29,891', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '49', '29,955', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '49', '30,209', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '49', '30,210', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '49', '30,211', 'INFO', 'save_model(model=ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,']\n",
      "['max_iter=1000, normalize=False, positive=False, precompute=False,']\n",
      "[\"random_state=123, selection='cyclic', tol=0.0001, warm_start=False), model_name=Trained Model, verbose=False)\"]\n",
      "['2020-07-29 09', '49', '30,211', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '49', '30,234', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '49', '30,256', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,']\n",
      "['max_iter=1000, normalize=False, positive=False, precompute=False,']\n",
      "[\"random_state=123, selection='cyclic', tol=0.0001, warm_start=False), None]\"]\n",
      "['2020-07-29 09', '49', '30,256', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '30,257', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '49', '30,444', 'INFO', 'Initializing Least Angle Regression']\n",
      "['2020-07-29 09', '49', '30,464', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '49', '30,487', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '30,514', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '30,521', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '30,521', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '30,546', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '49', '30,571', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '30,595', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '30,600', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '30,600', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '30,630', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '49', '30,652', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '30,670', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '30,677', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '30,678', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '30,704', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '49', '30,731', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '30,752', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '30,759', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '30,759', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '30,783', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '49', '30,813', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '30,830', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '30,838', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '30,839', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '30,858', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '49', '30,859', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '49', '30,898', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '49', '31,084', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '49', '31,084', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '49', '31,085', 'INFO', 'save_model(model=Lars(copy_X=True, eps=2.220446049250313e-16, fit_intercept=True, fit_path=True,']\n",
      "[\"jitter=None, n_nonzero_coefs=500, normalize=True, precompute='auto',\"]\n",
      "['random_state=None, verbose=False), model_name=Trained Model, verbose=False)']\n",
      "['2020-07-29 09', '49', '31,085', 'INFO', 'Appending prep pipeline']\n",
      "['2020-07-29 09', '49', '31,100', 'INFO', 'Trained Model.pkl saved in current working directory']\n",
      "['2020-07-29 09', '49', '31,113', 'INFO', '[Pipeline(memory=None,']\n",
      "[\"steps=[('dtypes',\"]\n",
      "['DataTypes_Auto_infer(categorical_features=[],']\n",
      "['display_types=True, features_todrop=[],']\n",
      "[\"ml_usecase='regression',\"]\n",
      "[\"numerical_features=[], target='charges',\"]\n",
      "['time_features=[])),']\n",
      "[\"('imputer',\"]\n",
      "[\"Simple_Imputer(categorical_strategy='not_available',\"]\n",
      "[\"numeric_strategy='mean',\"]\n",
      "['target_variable=None)),']\n",
      "[\"('new_levels1',\"]\n",
      "['New_Catagorical_Levels...']\n",
      "[\"('group', Empty()), ('nonliner', Empty()), ('scaling', Empty()),\"]\n",
      "[\"('P_transform', Empty()), ('pt_target', Empty()),\"]\n",
      "[\"('binn', Empty()), ('rem_outliers', Empty()),\"]\n",
      "[\"('cluster_all', Empty()), ('dummy', Dummify(target='charges')),\"]\n",
      "[\"('fix_perfect', Empty()), ('clean_names', Clean_Colum_Names()),\"]\n",
      "[\"('feature_select', Empty()), ('fix_multi', Empty()),\"]\n",
      "[\"('dfs', Empty()), ('pca', Empty())],\"]\n",
      "['verbose=False), Lars(copy_X=True, eps=2.220446049250313e-16, fit_intercept=True, fit_path=True,']\n",
      "[\"jitter=None, n_nonzero_coefs=500, normalize=True, precompute='auto',\"]\n",
      "['random_state=None, verbose=False), None]']\n",
      "['2020-07-29 09', '49', '31,114', 'INFO', 'save_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '31,114', 'INFO', 'SubProcess save_model() end ==================================']\n",
      "['2020-07-29 09', '49', '31,259', 'INFO', 'Initializing Lasso Least Angle Regression']\n",
      "['2020-07-29 09', '49', '31,273', 'INFO', 'Initializing Fold 1']\n",
      "['2020-07-29 09', '49', '31,290', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '31,303', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '31,307', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '31,307', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '31,322', 'INFO', 'Initializing Fold 2']\n",
      "['2020-07-29 09', '49', '31,337', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '31,349', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '31,353', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '31,354', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '31,371', 'INFO', 'Initializing Fold 3']\n",
      "['2020-07-29 09', '49', '31,385', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '31,397', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '31,401', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '31,401', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '31,418', 'INFO', 'Initializing Fold 4']\n",
      "['2020-07-29 09', '49', '31,435', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '31,447', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '31,454', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '31,454', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '31,476', 'INFO', 'Initializing Fold 5']\n",
      "['2020-07-29 09', '49', '31,493', 'INFO', 'Fitting Model']\n",
      "['2020-07-29 09', '49', '31,505', 'INFO', 'Evaluating Metrics']\n",
      "['2020-07-29 09', '49', '31,510', 'INFO', 'No inverse transformer found']\n",
      "['2020-07-29 09', '49', '31,510', 'INFO', 'Compiling Metrics']\n",
      "['2020-07-29 09', '49', '31,533', 'INFO', 'Calculating mean and std']\n",
      "['2020-07-29 09', '49', '31,535', 'INFO', 'Creating metrics dataframe']\n",
      "['2020-07-29 09', '49', '31,571', 'INFO', 'Creating MLFlow logs']\n",
      "['2020-07-29 09', '49', '31,659', 'INFO', \"IForest(behaviour='new', bootstrap=False, contamination=0.05,\"]\n",
      "[\"max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\"]\n",
      "['random_state=123, verbose=0)']\n",
      "['2020-07-29 09', '49', '31,660', 'INFO', 'deploy_model() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '31,676', 'INFO', 'Initializing get_config()']\n",
      "['2020-07-29 09', '49', '31,677', 'INFO', 'get_config(variable=X)']\n",
      "['2020-07-29 09', '49', '31,677', 'INFO', 'Global variable', 'X returned']\n",
      "['2020-07-29 09', '49', '31,678', 'INFO', 'get_config() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '31,728', 'INFO', 'Initializing get_config()']\n",
      "['2020-07-29 09', '49', '31,729', 'INFO', 'get_config(variable=seed)']\n",
      "['2020-07-29 09', '49', '31,729', 'INFO', 'Global variable', 'seed returned']\n",
      "['2020-07-29 09', '49', '31,729', 'INFO', 'get_config() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '31,747', 'INFO', 'Initializing set_config()']\n",
      "['2020-07-29 09', '49', '31,748', 'INFO', 'set_config(variable=seed, value=999)']\n",
      "['2020-07-29 09', '49', '31,748', 'INFO', 'Global variable', 'seed updated']\n",
      "['2020-07-29 09', '49', '31,748', 'INFO', 'set_config() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '31,762', 'INFO', 'Initializing get_config()']\n",
      "['2020-07-29 09', '49', '31,763', 'INFO', 'get_config(variable=seed)']\n",
      "['2020-07-29 09', '49', '31,764', 'INFO', 'Global variable', 'seed returned']\n",
      "['2020-07-29 09', '49', '31,764', 'INFO', 'get_config() succesfully completed......................................']\n",
      "['2020-07-29 09', '49', '31,768', 'INFO', 'SubProcess save_model() called ==================================']\n",
      "['2020-07-29 09', '49', '31,768', 'INFO', 'Initializing save_model()']\n",
      "['2020-07-29 09', '49', '31,770', 'INFO', 'save_model(model=LassoLars(alpha=1.0, copy_X=True, eps=2.220446049250313e-16, fit_intercept=True,']\n",
      "['fit_path=True, jitter=None, max_iter=500, normalize=True,']\n",
      "[\"positive=False, precompute='auto', random_state=None, verbose=False), model_name=Trained Model, verbose=False)\"]\n",
      "['2020-07-29 09', '49', '31,770', 'INFO', 'Appending prep pipeline']\n"
     ]
    }
   ],
   "source": [
    "get_system_logs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. MLFlow UI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "!mlflow ui"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End\n",
    "Thank you. For more information / tutorials on PyCaret, please visit https://www.pycaret.org"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pycaret-nightly-env",
   "language": "python",
   "name": "pycaret-nightly-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
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